The INTErventions, Research, and Action in Cities Team (INTERACT) is a national research collaboration of scientists, urban planners, and engaged citizens uncovering how the design of our cities is shaping the health and wellbeing of Canadians (www.teaminteract.ca). INTERACT is conducting longitudinal, mixed-methods natural experiment studies in four Canadian cities, with the aim of providing evidence on the impacts of urban transformations on people’s physical activity, social participation, and wellbeing, and inequalities in these outcomes.

Victoria’s All Ages and Abilities (AAA) Cycling Network introduces protected cycling infrastructure across the city, starting with the implementation of a 5.4km grid in the downtown core in 2018. Victoria’s $7.75M commitment to the AAA Cycling Network is an important step in its bold journey to becoming one of the best small cities for cycling in the world.

People were eligible to participate if they lived in the Capital Region and biked at least once a month in the City of Victoria. Exclusion criteria across all sites were being younger than 18 years old, not being able to read or write English (or English or French in Montreal) well enough to answer an online survey and any intention to move out of the region in the next two years. Participants were recruited through social media, postering, media articles, partner networks, snowball sampling, and in-person events.

Participants were entered into a prize draw to incentivize their participation, and received a 10 dollar giftcard upon complemention of both the Health and VERITAS surveys. Those who contributed data through the smart phone app or the Sensedoc received an additional 10 dollar giftcard.

Section 1: Transportation

Q1: How many bicycles are kept in your household?

Adult bikes

ggplot(d, aes(x = d$transp_bikes_adults)) + 
  geom_histogram(binwidth = 1, fill="#76D24A", na.rm = TRUE) + 
  xlab("Number of bicycles for adults") + 
##break value set for displaying integer values, which requires chekcing max value; can omit to use default scale 
  scale_x_continuous(breaks = c(0:20)) 

##print summary stats
summary(d$transp_bikes_adults)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   2.000   3.000   3.295   4.000  20.000

Children bikes

ggplot(d, aes(x = d$transp_bikes_kids)) + 
  geom_bar(binwidth = 1, fill="#76D24A", na.rm = TRUE) + xlab("Number of bicycles for children") + 
  scale_x_continuous(breaks= c(0:10))

summary(d$transp_bikes_kids)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.6548  1.0000 10.0000

Q2: On a scale of 1 to 5, with 1 being ‘very safe’ and 5 being ‘very dangerous’, overall, how safe do you think cycling is in your city?

#####PLOT DISTRIBUTION
##create Q2 response frequency table and convert to data frame
Q2 <- round(prop.table(table(factor(d$bike_safety, levels = 1:5)))*100,2)
Q2 <- as.data.frame(Q2)

##create response variable for plotting
Q2$response <- substring(row.names(Q2), 1)
Q2$response <- revalue(as.factor(Q2$response), c("1" = "Very safe", "2" = "Somewhat safe", "3" = "Neither safe nor unsafe", "4" = "Somewhat dangerous", "5" = "Very dangerous"))

##specify ggplot factor order for plot (default order is alphabetic)
Q2$plot <- factor(Q2$response, Q2$response)

##order responses as in Q2
p <- ggplot(Q2, aes(x = response, y = Freq, fill = response)) + theme(axis.text.x = element_text(size=12, angle=0, vjust = .6)  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)))

##get colors for plot (customizable, values can be specified from colorbrewer2.org, can also use scale fill palettes. See RColorBrewer documentation)
#cols <- c("#1a9641", "#a6d96a", "#ffffbf", "#fdae61", "#d7191c")

#create plot
p + geom_bar(aes(x = plot), data = Q2, stat = "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent of total") +
      xlab("Perception of bicycle risk") +
      ggtitle("")

#####CREATE ASSOCIATED TABLE
##summarize number of responses in each category
Q2.tb <- as.factor(d$bike_safety)
Q2.tb <- summary(Q2.tb)
Q2.tb <- as.data.frame(Q2.tb)

##create response variable for table 
Q2.tb$Var1 <- substring(row.names(Q2.tb), 1)
Q2.tb$response <- revalue(as.factor(Q2.tb$Var1), c("1" = "Very safe", "2" = "Somewhat safe", "3" = "Neither safe nor unsafe", "4" = "Somewhat dangerous", "5" = "Very dangerous"))

##merge with frequency table from above
plot.Q2 <- merge(Q2, Q2.tb, by = "response")

plot.Q2 <- plot.Q2 %>% arrange(Var1.x)
##drop intermediate columns
plot.Q2 <- plot.Q2[-c(2, 4, 6)]

plot.Q2 <- setcolorder(plot.Q2, c("response", "Q2.tb", "Freq"))

##rename columns
colnames(plot.Q2) <- c("Response", "N", "Proportion")

##print table
kable(plot.Q2) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Very safe 23 8.19
Somewhat safe 167 59.43
Neither safe nor unsafe 32 11.39
Somewhat dangerous 55 19.57
Very dangerous 4 1.42

Q3: How often do you typically travel by bicycle during each season?

d$bike_freq_a[d$bike_freq_a==-7] <- NA
d$bike_freq_b[d$bike_freq_b==-7] <- NA
d$bike_freq_c[d$bike_freq_c==-7] <- NA
d$bike_freq_d[d$bike_freq_d==-7] <- NA

d$bike_fall <- round(d$bike_freq_a/13)
d$bike_winter <- round(d$bike_freq_b/13)
d$bike_spring <- round(d$bike_freq_c/13)
d$bike_summer <- round(d$bike_freq_d/13)


fall <- ggplot(d, aes(x = d$bike_fall)) + geom_histogram (na.rm = TRUE, binwidth = 1, fill="#BF5B04") + xlab("Days per week in the fall")
winter <- ggplot(d, aes(x = d$bike_winter)) + geom_histogram (na.rm = TRUE, binwidth = 1, fill="#35AAF2") + xlab("Days per week in the winter")
spring <- ggplot(d, aes(x = d$bike_spring)) + geom_histogram (na.rm = TRUE, binwidth = 1, fill="#76D24A") + xlab("Days per week in the spring")
summer <- ggplot(d, aes(x = d$bike_summer)) + geom_histogram (na.rm = TRUE, binwidth = 1, fill="#F2B705") + xlab("Days per week in the summer")


grid.arrange(fall,winter,spring,summer)

Fall

summary(d$bike_fall)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   3.000   5.000   4.623   6.000   7.000

Winter

summary(d$bike_winter)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   2.000   4.000   3.689   5.000   7.000       1

Spring

summary(d$bike_spring)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   4.000   5.000   4.815   6.000   7.000

Summer

summary(d$bike_summer)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   4.750   5.000   5.214   6.000   7.000       1

Q4: How often are your cycling trips made with children?

bike.ch <- round(prop.table(table(factor(d$bike_children, levels = 1:4)))*100,2)
bike.ch <- as.data.frame(bike.ch)
bike.ch$response <- substring(row.names(bike.ch), 1)
bike.ch$response <- revalue(as.factor(bike.ch$response), c("1" = "Never", "2" = "Sometimes", "3" = "Often", "4" = "Always"))

bike.ch$plot <- factor(bike.ch$response, bike.ch$response)


ch <- ggplot(bike.ch, aes(x = response, y = Freq, fill = response)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
  guides(fill = FALSE) +
  scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("Bicyle trips with children") +
      ggtitle("")

ch + geom_bar(aes(x = plot), data = bike.ch, stat = "identity") 

trips.ch <- as.factor(d$bike_children)
trips.ch <- summary(trips.ch)
trips.ch <- as.data.frame(trips.ch)
trips.ch$Var1 <- substring(row.names(trips.ch), 1)
trips.ch$response <- revalue(as.factor(trips.ch$Var1), c("1" = "Never", "2" = "Sometimes", "3" = "Often", "4" = "Always"))



plot.ch <- merge(bike.ch, trips.ch, by = "response")

plot.ch <- plot.ch[-c(4,6)]
plot.ch <- setcolorder(plot.ch, c("response", "trips.ch", "Freq", "Var1.x"))
plot.ch <- plot.ch %>% arrange(Var1.x)
plot.ch <- plot.ch[-c(4)]

colnames(plot.ch) <- c("Response", "N", "Proportion")

kable(plot.ch) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Never 196 69.75
Sometimes 52 18.51
Often 29 10.32
Always 4 1.42

Q5: Do you currently use any of the following roads when you bike in Victoria?

Cook St or Fifth St

cook <- round(prop.table(table(factor(d$vicroads_a, levels = c("1", "2", "77"))))*100,2)
cook <- as.data.frame(cook)
cook$response <- substring(row.names(cook), 1)
cook$response <- revalue(as.factor(cook$response), c("1" = "Yes",  "2" = "No", "3" = "Don't know"))

cook$plot <- factor(cook$response, cook$response)

cook.plot <- ggplot(cook, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

cook.plot + geom_bar(aes(x = plot), data = cook, stat = "identity") 

cook.tb <- as.factor(d$vicroads_a)
cook.tb <- summary(cook.tb)
cook.tb <- as.data.frame(cook.tb)
cook.tb$Var1 <- substring(row.names(cook.tb), 1)
cook.tb$response <- revalue(as.factor(cook.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))

plot.cook <- merge(cook, cook.tb, by = "response")
plot.cook <- plot.cook[-c(2, 4, 6)]
plot.cook <- setcolorder(plot.cook, c("response", "cook.tb", "Freq"))
plot.cook$order <- c(3,2,1)
plot.cook <- plot.cook %>% arrange(order)
plot.cook <- plot.cook[-c(4)]
colnames(plot.cook) <- c("Response", "N", "Proportion")

kable(plot.cook) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 179 63.70
No 100 35.59
Don’t know 2 0.71

Fairfield Rd or Humboldt St

ffield <- round(prop.table(table(factor(d$vicroads_b, levels = c("1", "2", "77"))))*100,2)
ffield <- as.data.frame(ffield)
ffield$response <- substring(row.names(ffield), 1)
ffield$response <- revalue(as.factor(ffield$response), c("1" = "Yes",  "2" = "No", "3" = "Don't know"))

ffield$plot <- factor(ffield$response, ffield$response)

ffield.plot <- ggplot(ffield, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

ffield.plot + geom_bar(aes(x = plot), data = ffield, stat = "identity") 

ffield.tb <- as.factor(d$vicroads_b)
ffield.tb <- summary(ffield.tb)
ffield.tb <- as.data.frame(ffield.tb)
ffield.tb$Var1 <- substring(row.names(ffield.tb), 1)
ffield.tb$response <- revalue(as.factor(ffield.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))

plot.ffield <- merge(ffield, ffield.tb, by = "response")
plot.ffield <- plot.ffield[-c(2, 4, 6)]
plot.ffield <- setcolorder(plot.ffield, c("response", "ffield.tb", "Freq"))
plot.ffield$order <- c(3,2,1)
plot.ffield <- plot.ffield %>% arrange(order)
plot.ffield <- plot.ffield[-c(4)]
colnames(plot.ffield) <- c("Response", "N", "Proportion")

kable(plot.ffield) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 140 49.82
No 139 49.47
Don’t know 2 0.71

Fort St

fort <- round(prop.table(table(factor(d$vicroads_c, levels = c("1", "2", "77"))))*100,2)
fort <- as.data.frame(fort)
fort$response <- substring(row.names(fort), 1)
fort$response <- revalue(as.factor(fort$response), c("1" = "Yes",  "2" = "No", "3" = "Don't know"))

fort$plot <- factor(fort$response, fort$response)

fort.plot <- ggplot(fort, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("")

fort.plot + geom_bar(aes(x = plot), data = fort, stat = "identity")

fort.tb <- as.factor(d$vicroads_c)
fort.tb <- summary(fort.tb)
fort.tb <- as.data.frame(fort.tb)
fort.tb$Var1 <- substring(row.names(fort.tb), 1)

##intermediate step to insert response values where zero respondents chose that answer
# nval.df <- c("0") 
# nval.df <- as.data.frame(nval.df)
# nval.df$fort.tb <- as.factor(nval.df$nval.df)
# nval.df$Var1 <- c( "77")
# nval.df <- nval.df[-c(1)]

##coerce inserted values into table
# fort.tb <- rbind(fort.tb, nval.df)
fort.tb$response <- revalue(as.factor(fort.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))

plot.fort <- merge(fort, fort.tb, by = "response")
plot.fort <- plot.fort[-c(2, 4, 6)]
plot.fort <- setcolorder(plot.fort, c("response", "fort.tb", "Freq"))
plot.fort$order <- c(3, 2)
plot.fort <- plot.fort %>% arrange(order)
plot.fort <- plot.fort[-c(4)]
colnames(plot.fort) <- c("Response", "N", "Proportion")

kable(plot.fort) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 219 77.94
No 62 22.06

Government St

gvt <- round(prop.table(table(factor(d$vicroads_d, levels = c("1", "2", "77"))))*100,2)
gvt <- as.data.frame(gvt)
gvt$response <- substring(row.names(gvt), 1)
gvt$response <- revalue(as.factor(gvt$response), c("1" = "Yes",  "2" = "No", "3" = "Don't know"))

gvt$plot <- factor(gvt$response, gvt$response)

 
gvt.plot <- ggplot(gvt, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("")
      
gvt.plot + geom_bar(aes(x = plot), data = gvt, stat = "identity") 

gvt.tb <- as.factor(d$vicroads_d)
gvt.tb <- summary(gvt.tb)
gvt.tb <- as.data.frame(gvt.tb)
gvt.tb$Var1 <- substring(row.names(gvt.tb), 1)

gvt.tb$response <- revalue(as.factor(gvt.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))

plot.gvt <- merge(gvt, gvt.tb, by = "response")
plot.gvt <- plot.gvt[-c(4, 6)]
plot.gvt <- setcolorder(plot.gvt, c("response", "gvt.tb", "Freq", "Var1.x"))
plot.gvt <- plot.gvt %>% arrange(Var1.x)
plot.gvt <- plot.gvt[-c(4)]
colnames(plot.gvt) <- c("Response", "N", "Proportion")

kable(plot.gvt) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 214 76.16
No 67 23.84

Haultain St or Kings Rd

haultain <- round(prop.table(table(factor(d$vicroads_e, levels = c("1", "2", "77"))))*100,2)
haultain <- as.data.frame(haultain)
haultain$response <- substring(row.names(haultain), 1)
haultain$response <- revalue(as.factor(haultain$response), c("1" = "Yes",  "2" = "No", "3" = "Don't know"))

haultain$plot <- factor(haultain$response, haultain$response)


haultain.plot <- ggplot(haultain, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("")
      
haultain.plot + geom_bar(aes(x = plot), data = haultain, stat = "identity") 

haultain.tb <- as.factor(d$vicroads_e)
haultain.tb <- summary(haultain.tb)
haultain.tb <- as.data.frame(haultain.tb)
haultain.tb$Var1 <- substring(row.names(haultain.tb), 1)
haultain.tb$response <- revalue(as.factor(haultain.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))

plot.haultain <- merge(haultain, haultain.tb, by = "response")
plot.haultain <- plot.haultain[-c(2, 4, 6)]
plot.haultain <- setcolorder(plot.haultain, c("response", "haultain.tb", "Freq"))
plot.haultain$order <- c(3, 2, 1)
plot.haultain <- plot.haultain %>% arrange(order)
plot.haultain <- plot.haultain[-c(4)]
colnames(plot.haultain) <- c("Response", "N", "Proportion")

kable(plot.haultain) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 165 58.72
No 111 39.50
Don’t know 5 1.78

Pandora Ave

pand <- round(prop.table(table(factor(d$vicroads_f, levels = c("1", "2", "77"))))*100,2)
pand <- as.data.frame(pand)
pand$response <- substring(row.names(pand), 1)
pand$response <- revalue(as.factor(pand$response), c("1" = "Yes",  "2" = "No", "3" = "Don't know"))

pand$plot <- factor(pand$response, pand$response)

pand.plot <- ggplot(pand, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("")
      
pand.plot + geom_bar(aes(x = plot), data = pand, stat = "identity") 

pand.tb <- as.factor(d$vicroads_f)
pand.tb <- summary(pand.tb)
pand.tb <- as.data.frame(pand.tb)
pand.tb$Var1 <- substring(row.names(pand.tb), 1)

nval.df <- c("0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$pand.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "77")
nval.df <- nval.df[-c(1)]
pand.tb <- rbind(pand.tb, nval.df)
pand.tb$response <- revalue(as.factor(pand.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))

plot.pand <- merge(pand, pand.tb, by = "response")
plot.pand <- plot.pand[-c(2, 4, 6)]
plot.pand <- setcolorder(plot.pand, c("response", "pand.tb", "Freq"))
plot.pand$order <- c(3, 2, 1)
plot.pand <- plot.pand %>% arrange(order)
plot.pand <- plot.pand[-c(4)]
colnames(plot.pand) <- c("Response", "N", "Proportion")

kable(plot.pand) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 242 86.12
No 39 13.88
Don’t know 0 0.00

Shelbourne St or Begbie St

shelb <- round(prop.table(table(factor(d$vicroads_g, levels = c("1", "2", "77"))))*100,2)
shelb <- as.data.frame(shelb)
shelb$response <- substring(row.names(shelb), 1)
shelb$response <- revalue(as.factor(shelb$response), c("1" = "Yes",  "2" = "No", "3" = "Don't know"))

shelb$plot <- factor(shelb$response, shelb$response)

shelb.plot <- ggplot(shelb, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("")
      
shelb.plot + geom_bar(aes(x = plot), data = shelb, stat = "identity") 

shelb.tb <- as.factor(d$vicroads_g)
shelb.tb <- summary(shelb.tb)
shelb.tb <- as.data.frame(shelb.tb)
shelb.tb$Var1 <- substring(row.names(shelb.tb), 1)
shelb.tb$response <- revalue(as.factor(shelb.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))

plot.shelb <- merge(shelb, shelb.tb, by = "response")
plot.shelb <- plot.shelb[-c(2, 4, 6)]
plot.shelb <- setcolorder(plot.shelb, c("response", "shelb.tb", "Freq"))
plot.shelb$order <- c(3, 2, 1)
plot.shelb <- plot.shelb %>% arrange(order)
plot.shelb <- plot.shelb[-c(4)]
colnames(plot.shelb) <- c("Response", "N", "Proportion")

kable(plot.shelb) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 117 41.64
No 162 57.65
Don’t know 2 0.71

Harbour Rd, Wharf St or Belville St

bellv <- round(prop.table(table(factor(d$vicroads_h, levels = c("1", "2", "77"))))*100,2)
bellv <- as.data.frame(bellv)
bellv$response <- substring(row.names(bellv), 1)
bellv$response <- revalue(as.factor(bellv$response), c("1" = "Yes",  "2" = "No", "3" = "Don't know"))

bellv$plot <- factor(bellv$response, bellv$response)

bellv.plot <- ggplot(bellv, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("")
      
bellv.plot + geom_bar(aes(x = plot), data = bellv, stat = "identity") 

bellv.tb <- as.factor(d$vicroads_h)
bellv.tb <- summary(bellv.tb)
bellv.tb <- as.data.frame(bellv.tb)
bellv.tb$Var1 <- substring(row.names(bellv.tb), 1)
bellv.tb$response <- revalue(as.factor(bellv.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))

plot.bellv <- merge(bellv, bellv.tb, by = "response")
plot.bellv <- plot.bellv[-c(2, 4, 6)]
plot.bellv <- setcolorder(plot.bellv, c("response", "bellv.tb", "Freq"))
plot.bellv$order <- c(3, 2, 1)
plot.bellv <- plot.bellv %>% arrange(order)
plot.bellv <- plot.bellv[-c(4)]
colnames(plot.bellv) <- c("Response", "N", "Proportion")

kable(plot.bellv) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 220 78.29
No 59 21.00
Don’t know 2 0.71

Galloping Goose or the E&N Trail

goose <- round(prop.table(table(factor(d$vicroads_i, levels = c("1", "2", "77"), exclude = NULL)))*100,2)
goose <- as.data.frame(goose)
goose$response <- substring(row.names(goose), 1)
goose$response <- revalue(as.factor(goose$response), c("1" = "Yes",  "2" = "No", "3" = "Don't know"))

goose$plot <- factor(goose$response, goose$response)

goose.plot <- ggplot(goose, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("")
      
goose.plot + geom_bar(aes(x = plot), data = goose, stat = "identity") 

goose.tb <- as.factor(d$vicroads_i)
goose.tb <- summary(goose.tb)
goose.tb <- as.data.frame(goose.tb)
goose.tb$Var1 <- substring(row.names(goose.tb), 1)

nval.df <- c("0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$goose.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "77")
nval.df <- nval.df[-c(1)]
goose.tb <- rbind(goose.tb, nval.df)
goose.tb$response <- revalue(as.factor(goose.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))

plot.goose <- merge(goose, goose.tb, by = "response")
plot.goose <- plot.goose[-c(2, 4, 6)]
plot.goose <- setcolorder(plot.goose, c("response", "goose.tb", "Freq"))
plot.goose$order <- c(3, 2, 1)
plot.goose <- plot.goose %>% arrange(order)
plot.goose <- plot.goose[-c(4)]
colnames(plot.goose) <- c("Response", "N", "Proportion")

kable(plot.goose) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 245 87.19
No 36 12.81
Don’t know 0 0.00

Q6: How much would you agree with the following statement: “I would like to travel by bicycle more than I do now”?

bike.more <- round(prop.table(table(factor(d$bike_more, levels = c("1", "2", "3", "4", "77"))))*100,2)
bike.more <- as.data.frame(bike.more)
bike.more$group <- substring(row.names(bike.more), 1)
bike.more$group <- revalue(as.factor(bike.more$group), c("1" = "Strongly agree", "2" = "Somewhat agree", "3" = "Somewhat disagree", "4" = "Strongly disagree", "5" = "Don't know"))

bike.more$plot <- factor(bike.more$group, bike.more$group)

more <- ggplot(bike.more, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10))) 

more + geom_bar(aes(x = plot), data = bike.more, stat = "identity") +
  guides(fill = FALSE) +
  scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("Bicyle more") 

more.tb <- as.factor(d$bike_more)
more.tb <- summary(more.tb)
more.tb <- as.data.frame(more.tb)
more.tb$Var1 <- substring(row.names(more.tb), 1)
more.tb$group <- revalue(as.factor(more.tb$Var1), c("1" = "Strongly agree", "2" = "Somewhat agree", "3" = "Somewhat disagree", "4" = "Strongly disagree", "77" = "Don't know"))

more.plot <- merge(bike.more, more.tb, by = "group")
more.plot <- more.plot[-c(1:2, 6)]
more.plot <- setcolorder(more.plot, c("plot", "more.tb", "Freq"))
more.plot$order <- c(5, 2, 3, 1, 4)
more.plot <- more.plot %>% arrange(order)
more.plot <- more.plot[-c(4)]
colnames(more.plot) <- c("Response", "N", "Proportion")

kable(more.plot) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Strongly agree 109 38.79
Somewhat agree 126 44.84
Somewhat disagree 22 7.83
Strongly disagree 16 5.69
Don’t know 8 2.85

Q7: How comfortable would you feel biking in the following places?

###A path or trail that is physically separate from the street

path <- round(prop.table(table(factor(d$bike_comf_a, levels = c("1", "2", "3", "4", "77"))))*100,2)
path <- as.data.frame(path)
path$response <- substring(row.names(path), 1)
path$response <- revalue(as.factor(path$response), c("1" = "Very uncomfortable",  "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))

path$plot <- factor(path$response, path$response)

cols <- c("#d7191c","#fdae61", "#a6d96a", "#1a9641", "grey")
path.plot <- ggplot(path, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +  scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = cols) +
      ylab("Percent of total") +
      xlab("")
      
path.plot + geom_bar(aes(x = plot), data = path, stat = "identity") 

path.tb <- as.factor(d$bike_comf_a)
path.tb <- summary(path.tb)
path.tb <- as.data.frame(path.tb)
path.tb$Var1 <- substring(row.names(path.tb), 1)

path.tb$response <- revalue(as.factor(path.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "77" = "Don't know"))

plot.path <- merge(path, path.tb, by = "response")
plot.path <- plot.path[-c(4, 6)]
plot.path <- setcolorder(plot.path, c("response", "path.tb", "Freq", "Var1.x"))
plot.path <- plot.path %>% arrange(Var1.x)
plot.path <- plot.path[-c(4)]
colnames(plot.path) <- c("Response", "N", "Proportion")

kable(plot.path) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Very uncomfortable 21 7.47
Somewhat comfortable 18 6.41
Very comfortable 242 86.12

A quiet, residential street with traffic speeds of 30-40 km per hour

res <- prop.table(table(factor(d$bike_comf_b, levels = c("1", "2", "3", "4", "77"))))*100
res <- as.data.frame(res)
res$response <- substring(row.names(res), 1)
res$response <- revalue(as.factor(res$response), c("1" = "Very uncomfortable",  "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))

res$plot <- factor(res$response, res$response)

cols <- c("#d7191c","#fdae61", "#a6d96a", "#1a9641", "grey")
res.plot <- ggplot(res, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = cols) +
      ylab("Percent of total") +
      xlab("")
      
res.plot + geom_bar(aes(x = plot), data = res, stat = "identity") 

res.tb <- as.factor(d$bike_comf_b)
res.tb <- summary(res.tb)
res.tb <- as.data.frame(res.tb)
res.tb$Var1 <- substring(row.names(res.tb), 1)

res.tb$response <- revalue(as.factor(res.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))

plot.res <- merge(res, res.tb, by = "response")
plot.res <- plot.res[-c(4, 6)]
plot.res <- setcolorder(plot.res, c("response", "res.tb", "Freq", "Var1.x"))
plot.res <- plot.res %>% arrange(Var1.x)
plot.res <- plot.res[-c(4)]
colnames(plot.res) <- c("Response", "N", "Proportion")

kable(plot.res) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Very uncomfortable 21 7.473310
Somewhat uncomfortable 20 7.117438
Somewhat comfortable 87 30.960854
Very comfortable 153 54.448399

A quiet residential street, with a 30 km per hour speed limit, bicycle route markings, wide speed bumps, and other things that slow down and discourage car traffic

bkwy <- round(prop.table(table(factor(d$bike_comf_c, levels = c("1", "2", "3", "4", "77"))))*100,2)
bkwy <- as.data.frame(bkwy)
bkwy$response <- substring(row.names(bkwy), 1)
bkwy$response <- revalue(as.factor(bkwy$response), c("1" = "Very uncomfortable",  "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))

bkwy$plot <- factor(bkwy$response, bkwy$response)

cols <- c("#d7191c","#fdae61", "#a6d96a", "#1a9641", "grey")
bkwy.plot <- ggplot(bkwy, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = cols) +
      ylab("Percent of total") +
      xlab("")
      
bkwy.plot + geom_bar(aes(x = plot), data = bkwy, stat = "identity") 

bkwy.tb <- as.factor(d$bike_comf_c)
bkwy.tb <- summary(bkwy.tb)
bkwy.tb <- as.data.frame(bkwy.tb)
bkwy.tb$Var1 <- substring(row.names(bkwy.tb), 1)
bkwy.tb$response <- revalue(as.factor(bkwy.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "77" = "Don't know"))


plot.bkwy <- merge(bkwy, bkwy.tb, by = "response")
plot.bkwy <- plot.bkwy[-c(4, 6)]
plot.bkwy <- setcolorder(plot.bkwy, c("response", "bkwy.tb", "Freq", "Var1.x"))
plot.bkwy <- plot.bkwy %>% arrange(Var1.x)
plot.bkwy <- plot.bkwy[-c(4)]
colnames(plot.bkwy) <- c("Response", "N", "Proportion")

kable(plot.bkwy) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Very uncomfortable 22 7.83
Somewhat uncomfortable 4 1.42
Somewhat comfortable 39 13.88
Very comfortable 215 76.51
Don’t know 1 0.36

A major urban or suburban street with four lanes, on-street parking, traffic speeds of 50-60 km per hour, and no bike lane

major <- round(prop.table(table(factor(d$bike_comf_d, levels = c("1", "2", "3", "4", "77"))))*100,2)
major <- as.data.frame(major)
major$response <- substring(row.names(major), 1)
major$response <- revalue(as.factor(major$response), c("1" = "Very uncomfortable",  "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))

major$plot <- factor(major$response, major$response)

cols <- c("#d7191c","#fdae61", "#a6d96a", "#1a9641", "grey")
major.plot <- ggplot(major, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = cols) +
      ylab("Percent of total") +
      xlab("")
      
major.plot + geom_bar(aes(x = plot), data = major, stat = "identity") 

major.tb <- as.factor(d$bike_comf_d)
major.tb <- summary(major.tb)
major.tb <- as.data.frame(major.tb)
major.tb$Var1 <- substring(row.names(major.tb), 1)

# nval.df <- c("0") #insert missing values 
# nval.df <- as.data.frame(nval.df)
# nval.df$major.tb <- as.factor(nval.df$nval.df)
# nval.df$Var1 <- c( "77")
# nval.df <- nval.df[-c(1)]
# major.tb <- rbind(major.tb, nval.df)
major.tb$response <- revalue(as.factor(major.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "77" = "Don't know"))

plot.major <- merge(major, major.tb, by = "response")
plot.major <- plot.major[-c(2, 4, 6)]
plot.major <- setcolorder(plot.major, c("response", "major.tb", "Freq"))
plot.major$order <- c(4, 2, 3, 1)
plot.major <- plot.major %>% arrange(order)
plot.major <- plot.major[-c(4)]
colnames(plot.major) <- c("Response", "N", "Proportion")

kable(plot.major) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Very uncomfortable 127 45.20
Somewhat uncomfortable 106 37.72
Very comfortable 9 3.20
Somewhat comfortable 39 13.88

A major urban or suburban street with four lanes, on-street parking, traffic speeds of 50-60 km per hour, but with a striped bike lane added

lane <- round(prop.table(table(factor(d$bike_comf_e, levels = c("1", "2", "3", "4", "77"))))*100,2)
lane <- as.data.frame(lane)
lane$response <- substring(row.names(lane), 1)
lane$response <- revalue(as.factor(lane$response), c("1" = "Very uncomfortable",  "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))

lane$plot <- factor(lane$response, lane$response)

cols <- c("#d7191c","#fdae61", "#a6d96a", "#1a9641", "grey")
lane.plot <- ggplot(lane, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = cols) +
      ylab("Percent of total") +
      xlab("")
      
lane.plot + geom_bar(aes(x = plot), data = lane, stat = "identity") 

lane.tb <- as.factor(d$bike_comf_e)
lane.tb <- summary(lane.tb)
lane.tb <- as.data.frame(lane.tb)
lane.tb$Var1 <- substring(row.names(lane.tb), 1)

lane.tb$response <- revalue(as.factor(lane.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "77" = "Don't know"))

plot.lane <- merge(lane, lane.tb, by = "response")
plot.lane <- plot.lane[-c(4, 6)]
plot.lane <- setcolorder(plot.lane, c("response", "lane.tb", "Freq", "Var1.x"))
plot.lane <- plot.lane %>% arrange(Var1.x)
plot.lane <- plot.lane[-c(4)]
colnames(plot.lane) <- c("Response", "N", "Proportion")

kable(plot.lane) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Very uncomfortable 25 8.90
Somewhat uncomfortable 89 31.67
Somewhat comfortable 128 45.55
Very comfortable 39 13.88

A major urban or suburban street with four lanes, on-street parking, traffic speeds of 50-60 km per hour, but with a wide bike lane physically separated from traffic by a raised curb, planters, or parked cars

track <- round(prop.table(table(factor(d$bike_comf_f, levels = c("1", "2", "3", "4", "77"))))*100,2)
track <- as.data.frame(track)
track$response <- substring(row.names(track), 1)
track$response <- revalue(as.factor(track$response), c("1" = "Very uncomfortable",  "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))

track$plot <- factor(track$response, track$response)

cols <- c("#d7191c","#fdae61", "#a6d96a", "#1a9641", "grey")
track.plot <- ggplot(track, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = cols) +
      ylab("Percent of total") +
      xlab("")
      
track.plot + geom_bar(aes(x = plot), data = track, stat = "identity") 

track.tb <- as.factor(d$bike_comf_f)
track.tb <- summary(track.tb)
track.tb <- as.data.frame(track.tb)
track.tb$Var1 <- substring(row.names(track.tb), 1)
track.tb$response <- revalue(as.factor(track.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "77" = "Don't know"))

plot.track <- merge(track, track.tb, by = "response")
plot.track <- plot.track[-c(2, 4, 6)]
plot.track <- setcolorder(plot.track, c("response", "track.tb", "Freq"))
plot.track$order <- c(5, 3, 2, 4, 1)
plot.track <- plot.track %>% arrange(order)
plot.track <- plot.track[-c(4)]
colnames(plot.track) <- c("Response", "N", "Proportion")

kable(plot.track) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Very uncomfortable 20 7.12
Somewhat uncomfortable 9 3.20
Somewhat comfortable 58 20.64
Very comfortable 191 67.97
Don’t know 3 1.07

Q8: Have you ever heard of the All Ages and Abilities (AAA) Network (formerly known as Biketoria)?

AAA <- round(prop.table(table(factor(d$aaa_familiarity, levels = c("1", "2"))))*100,2)
AAA <- as.data.frame(AAA)
AAA$response <- substring(row.names(AAA), 1)
AAA$response <- revalue(as.factor(AAA$response), c("1" = "Yes",  "2" = "No"))

AAA$plot <- factor(AAA$response, AAA$response)

AAA.plot <- ggplot(AAA, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) 

AAA.plot + geom_bar(aes(x = plot), data = AAA, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

AAA.tb <- as.factor(d$aaa_familiarity)
AAA.tb <- summary(AAA.tb)
AAA.tb <- as.data.frame(AAA.tb)
AAA.tb$Var1 <- substring(row.names(AAA.tb), 1)
AAA.tb$response <- revalue(as.factor(AAA.tb$Var1), c("1" = "Yes", "2" = "No"))

plot.AAA <- merge(AAA, AAA.tb, by = "response")
plot.AAA <- plot.AAA[-c(2, 4, 6)]
plot.AAA <- setcolorder(plot.AAA, c("response", "AAA.tb", "Freq"))
plot.AAA$order <- c(2, 1)
plot.AAA <- plot.AAA %>% arrange(order)
plot.AAA <- plot.AAA[-c(4)]
colnames(plot.AAA) <- c("Response", "N", "Proportion")

kable(plot.AAA) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 189 67.26
No 92 32.74

Q9: Do you think that the All Ages and Abilities (AAA) Network is a good or bad idea for Victoria? It is a:

aaa.idea <- round(prop.table(table(factor(d$aaa_idea, levels = c("1", "2", "3", "4")), exclude=NULL))*100,2)
aaa.idea <- as.data.frame(aaa.idea)
aaa.idea$response <- substring(row.names(aaa.idea), 1)
aaa.idea$response <- revalue(as.factor(aaa.idea$response), c("1" = "Very good idea",  "2" = "Somewhat good idea", "3" = "Somewhat bad idea", "4" = "Very bad idea", "5" = "Don't know"))

aaa.idea$plot <- factor(aaa.idea$response, aaa.idea$response)

aaa.idea.plot <- ggplot(aaa.idea, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade)+
      ylab("Percent of total") +
      xlab("")
      
aaa.idea.plot + geom_bar(aes(x = plot), data = aaa.idea, stat = "identity") 

aaa.idea.tb <- as.factor(d$aaa_idea)
aaa.idea.tb <- summary(aaa.idea.tb)
aaa.idea.tb <- as.data.frame(aaa.idea.tb)
aaa.idea.tb$Var1 <- substring(row.names(aaa.idea.tb), 1)

nval.df <- c("0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$aaa.idea.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "4")
nval.df <- nval.df[-c(1)]
aaa.idea.tb <- rbind(aaa.idea.tb, nval.df)

aaa.idea.tb$response <- revalue(as.factor(aaa.idea.tb$Var1), c("1" = "Very good idea",  "2" = "Somewhat good idea", "3" = "Somewhat bad idea", "4" = "Very bad idea", "77" = "Don't know"))

plot.aaa.idea <- merge(aaa.idea, aaa.idea.tb, by = "response")
plot.aaa.idea <- plot.aaa.idea[-c(4, 6)]
plot.aaa.idea <- setcolorder(plot.aaa.idea, c("response", "aaa.idea.tb", "Freq", "Var1.x"))
plot.aaa.idea <- plot.aaa.idea %>% arrange(Var1.x)
plot.aaa.idea <- plot.aaa.idea[-c(4)]
colnames(plot.aaa.idea) <- c("Response", "N", "Proportion")

kable(plot.aaa.idea) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Very good idea 243 86.48
Somewhat good idea 31 11.03
Somewhat bad idea 3 1.07
Very bad idea 1 0.36
Very bad idea 0 0.36
Don’t know 3 1.07

Q10: Will you likely cycle more in the future once the All Ages and Abilities (AAA) Network is built?

bike.more <- round(prop.table(table(factor(d$aaa_bike_more, levels = c("1", "2"))))*100,2)
bike.more <- as.data.frame(bike.more)
bike.more$response <- substring(row.names(bike.more), 1)
bike.more$response <- revalue(as.factor(bike.more$response), c("1" = "Yes",  "2" = "No"))

bike.more$plot <- factor(bike.more$response, bike.more$response)

bike.more.plot <- ggplot(bike.more, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) 

bike.more.plot + geom_bar(aes(x = plot), data = bike.more, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

bike.more.tb <- as.factor(d$aaa_bike_more)
bike.more.tb <- summary(bike.more.tb)
bike.more.tb <- as.data.frame(bike.more.tb)
bike.more.tb$Var1 <- substring(row.names(bike.more.tb), 1)
bike.more.tb$response <- revalue(as.factor(bike.more.tb$Var1), c("1" = "Yes", "2" = "No"))

plot.bike.more <- merge(bike.more, bike.more.tb, by = "response")
plot.bike.more <- plot.bike.more[-c(2, 4, 6)]
plot.bike.more <- setcolorder(plot.bike.more, c("response", "bike.more.tb", "Freq"))
plot.bike.more$order <- c(2, 1)
plot.bike.more <- plot.bike.more %>% arrange(order)
plot.bike.more <- plot.bike.more[-c(4)]
colnames(plot.bike.more) <- c("Response", "N", "Proportion")

kable(plot.bike.more) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 221 78.65
No 60 21.35

Q11: Do you currently have a valid driver’s license?

license <- round(prop.table(table(factor(d$license, levels = c("1", "2"))))*100,2)
license <- as.data.frame(license)
license$response <- substring(row.names(license), 1)
license$response <- revalue(as.factor(license$response), c("1" = "Yes",  "2" = "No"))

license$plot <- factor(license$response, license$response)

license.plot <- ggplot(license, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) 

license.plot + geom_bar(aes(x = plot), data = license, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

license.tb <- as.factor(d$license)
license.tb <- summary(license.tb)
license.tb <- as.data.frame(license.tb)
license.tb$Var1 <- substring(row.names(license.tb), 1)
license.tb$response <- revalue(as.factor(license.tb$Var1), c("1" = "Yes", "2" = "No"))

plot.license <- merge(license, license.tb, by = "response")
plot.license <- plot.license[-c(2, 4, 6)]
plot.license <- setcolorder(plot.license, c("response", "license.tb", "Freq"))
plot.license$order <- c(2, 1)
plot.license <- plot.license %>% arrange(order)
plot.license <- plot.license[-c(4)]
colnames(plot.license) <- c("Response", "N", "Proportion")

kable(plot.license) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 273 97.15
No 8 2.85

Q12: Do you have access to a car?

access <- round(prop.table(table(factor(d$car_access, levels = c("1", "2", "-7")), exclude=NULL))*100,2)
access <- as.data.frame(access)
access$response <- substring(row.names(access), 1)
access$response <- revalue(as.factor(access$response), c("1" = "Yes",  "2" = "No", "3" = "N/A"))

access$plot <- factor(access$response, access$response)

access.plot <- ggplot(access, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) 

access.plot + geom_bar(aes(x = plot), data = access, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

access.tb <- as.factor(d$car_access)
access.tb <- summary(access.tb)

access.tb <- as.data.frame(access.tb)
access.tb$Var1 <- substring(row.names(access.tb), 1)
access.tb$response <- revalue(as.factor(access.tb$Var1), c("1" = "Yes", "2" = "No", "-7" = "N/A"))

plot.access <- merge(access, access.tb, by = "response")
plot.access <- plot.access[-c(4, 6)]
plot.access <- setcolorder(plot.access, c("response", "access.tb", "Freq", "Var1.x"))
plot.access <- plot.access %>% arrange(Var1.x)
plot.access <- plot.access[-c(4)]
colnames(plot.access) <- c("Response", "N", "Proportion")

kable(plot.access) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 255 90.75
No 18 6.41
N/A 8 2.85

Q13: How many cars, trucks, or vans are kept in your household?

d$cars_household[d$cars_household==-7] <- NA
ggplot(d, aes(x = d$cars_household)) + geom_bar(na.rm = TRUE, fill="#76D24A") + guides(fill = FALSE) +  xlab("Number of cars, trucks or vans in household")

summary(d$cars_household)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   1.000   1.306   2.000   4.000      26

Q14: How much do you enjoy using each transportation mode?

Walking

#preferred_mode_a walking

preferred_mode_a <- round(prop.table(table(factor(d$preferred_mode_a, levels = c("1", "2", "3", "4", "5"))))*100,2)

preferred_mode_a <- as.data.frame(preferred_mode_a)
preferred_mode_a$group <- substring(row.names(preferred_mode_a), 1)
preferred_mode_a$group <- revalue(as.character(preferred_mode_a$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

preferred_mode_a$plot <- factor(preferred_mode_a$group, preferred_mode_a$group)

preferred_mode_a.plot <- ggplot(preferred_mode_a, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
preferred_mode_a.plot + geom_bar(aes(x = plot), data = preferred_mode_a, stat = "identity") 

preferred_mode_a.tb <- as.factor(d$preferred_mode_a)
preferred_mode_a.tb <- summary(preferred_mode_a.tb)
preferred_mode_a.tb <- as.data.frame(preferred_mode_a.tb)
preferred_mode_a.tb$Var1 <- substring(row.names(preferred_mode_a.tb), 1)
preferred_mode_a.tb$group <- revalue(as.character(preferred_mode_a.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable"))
plot.preferred_mode_a <- merge(preferred_mode_a, preferred_mode_a.tb, by = "group")
plot.preferred_mode_a <- plot.preferred_mode_a[-c(2, 4, 6)]
plot.preferred_mode_a <- setcolorder(plot.preferred_mode_a, c("group", "preferred_mode_a.tb", "Freq"))

colnames(plot.preferred_mode_a) <- c("Response", "N", "Proportion")

kable(plot.preferred_mode_a) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
1 A lot 159 56.58
2 92 32.74
3 21 7.47
4 Not at all 7 2.49
Not applicable 2 0.71

Biking

#preferred_mode_b biking

preferred_mode_b <- round(prop.table(table(factor(d$preferred_mode_b, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100,2)
preferred_mode_b <- as.data.frame(preferred_mode_b)
preferred_mode_b$group <- substring(row.names(preferred_mode_b), 1)
preferred_mode_b$group <- revalue(as.character(preferred_mode_b$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

preferred_mode_b$plot <- factor(preferred_mode_b$group, preferred_mode_b$group)

preferred_mode_b.plot <- ggplot(preferred_mode_b, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
preferred_mode_b.plot + geom_bar(aes(x = plot), data = preferred_mode_b, stat = "identity") 

preferred_mode_b.tb <- as.factor(d$preferred_mode_b)
preferred_mode_b.tb <- summary(preferred_mode_b.tb)
preferred_mode_b.tb <- as.data.frame(preferred_mode_b.tb)
preferred_mode_b.tb$Var1 <- substring(row.names(preferred_mode_b.tb), 1)

nval.df <- c("0", "0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$preferred_mode_b.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "4", "5")
nval.df <- nval.df[-c(1)]
preferred_mode_b.tb <- rbind(preferred_mode_b.tb, nval.df)

preferred_mode_b.tb$group <- revalue(as.character(preferred_mode_b.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable"))
plot.preferred_mode_b <- merge(preferred_mode_b, preferred_mode_b.tb, by = "group")
plot.preferred_mode_b <- plot.preferred_mode_b[-c(2, 4)]
plot.preferred_mode_b <- setcolorder(plot.preferred_mode_b, c("group", "preferred_mode_b.tb", "Freq", "Var1.y"))
plot.preferred_mode_b <- plot.preferred_mode_b[-c(4)]
colnames(plot.preferred_mode_b) <- c("Response", "N", "Proportion")

kable(plot.preferred_mode_b) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
1 A lot 248 88.26
2 30 10.68
3 3 1.07
4 Not at all 0 0.00
Not applicable 0 0.00

Public transit

#preferred_mode_c public transit

preferred_mode_c <- round(prop.table(table(factor(d$preferred_mode_c, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100,2)
preferred_mode_c <- as.data.frame(preferred_mode_c)
preferred_mode_c$group <- substring(row.names(preferred_mode_c), 1)
preferred_mode_c$group <- revalue(as.character(preferred_mode_c$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

preferred_mode_c$plot <- factor(preferred_mode_c$group, preferred_mode_c$group)

preferred_mode_c.plot <- ggplot(preferred_mode_c, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
preferred_mode_c.plot + geom_bar(aes(x = plot), data = preferred_mode_c, stat = "identity") 

preferred_mode_c.tb <- as.factor(d$preferred_mode_c)
preferred_mode_c.tb <- summary(preferred_mode_c.tb)
preferred_mode_c.tb <- as.data.frame(preferred_mode_c.tb)
preferred_mode_c.tb$Var1 <- substring(row.names(preferred_mode_c.tb), 1)
preferred_mode_c.tb$group <- revalue(as.character(preferred_mode_c.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable"))
plot.preferred_mode_c <- merge(preferred_mode_c, preferred_mode_c.tb, by = "group")
plot.preferred_mode_c <- plot.preferred_mode_c[-c(2, 4, 6)]
plot.preferred_mode_c <- setcolorder(plot.preferred_mode_c, c("group", "preferred_mode_c.tb", "Freq"))

colnames(plot.preferred_mode_c) <- c("Response", "N", "Proportion")

kable(plot.preferred_mode_c) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
1 A lot 11 3.91
2 65 23.13
3 130 46.26
4 Not at all 65 23.13
Not applicable 10 3.56

Car

#preferred_mode_d car

preferred_mode_d <- round(prop.table(table(factor(d$preferred_mode_d, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100,2)
preferred_mode_d <- as.data.frame(preferred_mode_d)
preferred_mode_d$group <- substring(row.names(preferred_mode_d), 1)
preferred_mode_d$group <- revalue(as.character(preferred_mode_d$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

preferred_mode_d$plot <- factor(preferred_mode_d$group, preferred_mode_d$group)

preferred_mode_d.plot <- ggplot(preferred_mode_d, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
preferred_mode_d.plot + geom_bar(aes(x = plot), data = preferred_mode_d, stat = "identity") 

preferred_mode_d.tb <- as.factor(d$preferred_mode_d)
preferred_mode_d.tb <- summary(preferred_mode_d.tb)
preferred_mode_d.tb <- as.data.frame(preferred_mode_d.tb)
preferred_mode_d.tb$Var1 <- substring(row.names(preferred_mode_d.tb), 1)
preferred_mode_d.tb$group <- revalue(as.character(preferred_mode_d.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable"))
plot.preferred_mode_d <- merge(preferred_mode_d, preferred_mode_d.tb, by = "group")
plot.preferred_mode_d <- plot.preferred_mode_d[-c(2, 4, 6)]
plot.preferred_mode_d <- setcolorder(plot.preferred_mode_d, c("group", "preferred_mode_d.tb", "Freq"))

colnames(plot.preferred_mode_d) <- c("Response", "N", "Proportion")

kable(plot.preferred_mode_d) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
1 A lot 31 11.03
2 98 34.88
3 111 39.50
4 Not at all 35 12.46
Not applicable 6 2.14

Motorcycle or scooter

#preferred_mode_e motorcycle or scooter

preferred_mode_e <- round(prop.table(table(factor(d$preferred_mode_e, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100,2)
preferred_mode_e <- as.data.frame(preferred_mode_e)
preferred_mode_e$group <- substring(row.names(preferred_mode_e), 1)
preferred_mode_e$group <- revalue(as.character(preferred_mode_e$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

preferred_mode_e$plot <- factor(preferred_mode_e$group, preferred_mode_e$group)

preferred_mode_e.plot <- ggplot(preferred_mode_e, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
preferred_mode_e.plot + geom_bar(aes(x = plot), data = preferred_mode_e, stat = "identity") 

preferred_mode_e.tb <- as.factor(d$preferred_mode_e)
preferred_mode_e.tb <- summary(preferred_mode_e.tb)
preferred_mode_e.tb <- as.data.frame(preferred_mode_e.tb)
preferred_mode_e.tb$Var1 <- substring(row.names(preferred_mode_e.tb), 1)
preferred_mode_e.tb$group <- revalue(as.character(preferred_mode_e.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable"))
plot.preferred_mode_e <- merge(preferred_mode_e, preferred_mode_e.tb, by = "group")
plot.preferred_mode_e <- plot.preferred_mode_e[-c(2, 4, 6)]
plot.preferred_mode_e <- setcolorder(plot.preferred_mode_e, c("group", "preferred_mode_e.tb", "Freq"))

colnames(plot.preferred_mode_e) <- c("Response", "N", "Proportion")

kable(plot.preferred_mode_e) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
1 A lot 8 2.85
2 14 4.98
3 8 2.85
4 Not at all 29 10.32
Not applicable 222 79.00

Q15: Even if you do not personally use this mode of transportation, do you find cycling in your city to be.

a. Safe?

# perception_cycling_a 

perception_cycling_a <- round(prop.table(table(factor(d$perception_cycling_a, levels = c("1", "2", "3", "4", "77")), exclude=NULL))*100,2)
perception_cycling_a <- as.data.frame(perception_cycling_a)
perception_cycling_a$group <- substring(row.names(perception_cycling_a), 1)
perception_cycling_a$group <- revalue(as.character(perception_cycling_a$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

perception_cycling_a$plot <- factor(perception_cycling_a$group, perception_cycling_a$group)

perception_cycling_a.plot <- ggplot(perception_cycling_a, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
perception_cycling_a.plot + geom_bar(aes(x = plot), data = perception_cycling_a, stat = "identity") 

perception_cycling_a.tb <- as.factor(d$perception_cycling_a)
perception_cycling_a.tb <- summary(perception_cycling_a.tb)
perception_cycling_a.tb <- as.data.frame(perception_cycling_a.tb)
perception_cycling_a.tb$Var1 <- substring(row.names(perception_cycling_a.tb), 1)

nval.df <- c("0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$perception_cycling_a.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "77")
nval.df <- nval.df[-c(1)]
perception_cycling_a.tb <- rbind(perception_cycling_a.tb, nval.df)

perception_cycling_a.tb$group <- revalue(as.character(perception_cycling_a.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "77" = "Not applicable"))
plot.perception_cycling_a <- merge(perception_cycling_a, perception_cycling_a.tb, by = "group")
plot.perception_cycling_a <- plot.perception_cycling_a[-c(2, 4, 6)]
plot.perception_cycling_a <- setcolorder(plot.perception_cycling_a, c("group", "perception_cycling_a.tb", "Freq"))

colnames(plot.perception_cycling_a) <- c("Response", "N", "Proportion")

kable(plot.perception_cycling_a) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Not applicable 0 NaN

b. Reliable

# perception_cycling_b

perception_cycling_b <- round(prop.table(table(factor(d$perception_cycling_b, levels = c("1", "2", "3", "4", "77")), exclude=NULL))*100,2)
perception_cycling_b <- as.data.frame(perception_cycling_b)
perception_cycling_b$group <- substring(row.names(perception_cycling_b), 1)
perception_cycling_b$group <- revalue(as.character(perception_cycling_b$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

perception_cycling_b$plot <- factor(perception_cycling_b$group, perception_cycling_b$group)

perception_cycling_b.plot <- ggplot(perception_cycling_b, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
perception_cycling_b.plot + geom_bar(aes(x = plot), data = perception_cycling_b, stat = "identity") 

perception_cycling_b.tb <- as.factor(d$perception_cycling_b)
perception_cycling_b.tb <- summary(perception_cycling_b.tb)
perception_cycling_b.tb <- as.data.frame(perception_cycling_b.tb)
perception_cycling_b.tb$Var1 <- substring(row.names(perception_cycling_b.tb), 1)

# nval.df <- c("0", "0") #insert missing values 
# nval.df <- as.data.frame(nval.df)
# nval.df$perception_cycling_b.tb <- as.factor(nval.df$nval.df)
# nval.df$Var1 <- c( "4", "77")
# nval.df <- nval.df[-c(1)]
# perception_cycling_b.tb <- rbind(perception_cycling_b.tb, nval.df)

perception_cycling_b.tb$group <- revalue(as.character(perception_cycling_b.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "77" = "Not applicable"))
plot.perception_cycling_b <- merge(perception_cycling_b, perception_cycling_b.tb, by = "group")
plot.perception_cycling_b <- plot.perception_cycling_b[-c(2, 4, 6)]
plot.perception_cycling_b <- setcolorder(plot.perception_cycling_b, c("group", "perception_cycling_b.tb", "Freq"))

colnames(plot.perception_cycling_b) <- c("Response", "N", "Proportion")

kable(plot.perception_cycling_b) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion

c. Practical?

perception_cycling_c <- round(prop.table(table(factor(d$perception_cycling_c, levels = c("1", "2", "3", "4", "77")), exclude=NULL))*100,2)
perception_cycling_c <- as.data.frame(perception_cycling_c)
perception_cycling_c$group <- substring(row.names(perception_cycling_c), 1)
perception_cycling_c$group <- revalue(as.character(perception_cycling_c$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

perception_cycling_c$plot <- factor(perception_cycling_c$group, perception_cycling_c$group)

perception_cycling_c.plot <- ggplot(perception_cycling_c, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
perception_cycling_c.plot + geom_bar(aes(x = plot), data = perception_cycling_c, stat = "identity") 

perception_cycling_c.tb <- as.factor(d$perception_cycling_c)
perception_cycling_c.tb <- summary(perception_cycling_c.tb)
perception_cycling_c.tb <- as.data.frame(perception_cycling_c.tb)
perception_cycling_c.tb$Var1 <- substring(row.names(perception_cycling_c.tb), 1)

nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$perception_cycling_c.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "77")
nval.df <- nval.df[-c(1)]
perception_cycling_c.tb <- rbind(perception_cycling_c.tb, nval.df)

perception_cycling_c.tb$group <- revalue(as.character(perception_cycling_c.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "77" = "Not applicable"))
plot.perception_cycling_c <- merge(perception_cycling_c, perception_cycling_c.tb, by = "group")
plot.perception_cycling_c <- plot.perception_cycling_c[-c(2, 4, 6)]
plot.perception_cycling_c <- setcolorder(plot.perception_cycling_c, c("group", "perception_cycling_c.tb", "Freq"))

colnames(plot.perception_cycling_c) <- c("Response", "N", "Proportion")

kable(plot.perception_cycling_c) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Not applicable 0 NaN

d. Enjoyable?

perception_cycling_d <- round(prop.table(table(factor(d$perception_cycling_d, levels = c("1", "2", "3", "4", "77")), exclude=NULL))*100,2)
perception_cycling_d <- as.data.frame(perception_cycling_d)
perception_cycling_d$group <- substring(row.names(perception_cycling_d), 1)
perception_cycling_d$group <- revalue(as.character(perception_cycling_d$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

perception_cycling_d$plot <- factor(perception_cycling_d$group, perception_cycling_d$group)

perception_cycling_d.plot <- ggplot(perception_cycling_d, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
perception_cycling_d.plot + geom_bar(aes(x = plot), data = perception_cycling_d, stat = "identity") 

perception_cycling_d.tb <- as.factor(d$perception_cycling_d)
perception_cycling_d.tb <- summary(perception_cycling_d.tb)
perception_cycling_d.tb <- as.data.frame(perception_cycling_d.tb)
perception_cycling_d.tb$Var1 <- substring(row.names(perception_cycling_d.tb), 1)

nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$perception_cycling_d.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "77")
nval.df <- nval.df[-c(1)]
perception_cycling_d.tb <- rbind(perception_cycling_d.tb, nval.df)

perception_cycling_d.tb$group <- revalue(as.character(perception_cycling_d.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "77" = "Not applicable"))
plot.perception_cycling_d <- merge(perception_cycling_d, perception_cycling_d.tb, by = "group")
plot.perception_cycling_d <- plot.perception_cycling_d[-c(2, 4, 6)]
plot.perception_cycling_d <- setcolorder(plot.perception_cycling_d, c("group", "perception_cycling_d.tb", "Freq"))

colnames(plot.perception_cycling_d) <- c("Response", "N", "Proportion")

kable(plot.perception_cycling_d) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Not applicable 0 NaN

e. Affordable?

perception_cycling_e <- round(prop.table(table(factor(d$perception_cycling_e, levels = c("1", "2", "3", "4", "77")), exclude=NULL))*100,2)
perception_cycling_e <- as.data.frame(perception_cycling_e)
perception_cycling_e$group <- substring(row.names(perception_cycling_e), 1)
perception_cycling_e$group <- revalue(as.character(perception_cycling_e$group), c("1" = "1 A lot", "4" = "4 Not at all", "5" = "Not applicable"))

perception_cycling_e$plot <- factor(perception_cycling_e$group, perception_cycling_e$group)

perception_cycling_e.plot <- ggplot(perception_cycling_e, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
perception_cycling_e.plot + geom_bar(aes(x = plot), data = perception_cycling_e, stat = "identity") 

perception_cycling_e.tb <- as.factor(d$perception_cycling_e)
perception_cycling_e.tb <- summary(perception_cycling_e.tb)
perception_cycling_e.tb <- as.data.frame(perception_cycling_e.tb)
perception_cycling_e.tb$Var1 <- substring(row.names(perception_cycling_e.tb), 1)

nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$perception_cycling_e.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "4", "77")
nval.df <- nval.df[-c(1)]
perception_cycling_e.tb <- rbind(perception_cycling_e.tb, nval.df)

perception_cycling_e.tb$group <- revalue(as.character(perception_cycling_e.tb$Var1), c("1" = "1 A lot",  "2" = "2", "3" = "3", "4" = "4 Not at all", "77" = "Not applicable"))
plot.perception_cycling_e <- merge(perception_cycling_e, perception_cycling_e.tb, by = "group")
plot.perception_cycling_e <- plot.perception_cycling_e[-c(2, 4, 6)]
plot.perception_cycling_e <- setcolorder(plot.perception_cycling_e, c("group", "perception_cycling_e.tb", "Freq"))

colnames(plot.perception_cycling_e) <- c("Response", "N", "Proportion")

kable(plot.perception_cycling_e) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
4 Not at all 0 NaN
Not applicable 0 NaN 
carshare.df <- c("Car2Go", "Modo", "ZipCar", "Other") 
carshare.df <- as.data.frame(carshare.df)
carshare.df$Members <- c("21", "46", "5", "7")
as.numeric(carshare.df$Members)
colnames(carshare.df) <- c("Carshare", "N Respondents")

Q16:Which car share services are you a member of?

noshare <- round(prop.table(table(factor(d$car_share_5, levels = c("0", "1"))))*100,2)
noshare <- as.data.frame(noshare)
noshare$response <- substring(row.names(noshare), 1)
noshare$response <- revalue(as.factor(noshare$response), c("1" = "Member",  "2" = "Non-member"))

noshare$plot <- factor(noshare$response, noshare$response)


noshare.plot <- ggplot(noshare, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(angle=90, vjust=.6)) 

noshare.plot + geom_bar(aes(x = plot), data = noshare, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response") +
      ggtitle("Car share membership")

noshare.tb <- as.factor(d$car_share_5)
noshare.tb <- summary(noshare.tb)
noshare.tb <- as.data.frame(noshare.tb)
noshare.tb$Var1 <- substring(row.names(noshare.tb), 1)
noshare.tb$response <- revalue(as.factor(noshare.tb$Var1), c("0" = "Member", "1" = "Non-member"))

plot.noshare <- merge(noshare, noshare.tb, by = "response")
plot.noshare <- plot.noshare[-c(2, 4, 6)]
plot.noshare <- setcolorder(plot.noshare, c("response", "noshare.tb", "Freq"))
plot.noshare$order <- c(1, 2)
plot.noshare <- plot.noshare %>% arrange(order)
plot.noshare <- plot.noshare[-c(4)]
colnames(plot.noshare) <- c("Response", "N", "Proportion")

kable(plot.noshare) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Member 58 20.64
Non-member 223 79.36 
kable(carshare.df) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Carshare N Respondents
Car2Go 21
Modo 46
ZipCar 5
Other 7

Q17: Are you a member of a bike advocacy group (e.g., Greater Victoria Cycling Coalition)?

advocacy <- round(prop.table(table(factor(d$bike_advocacy, levels = c("1", "2"))))*100,2)
advocacy <- as.data.frame(advocacy)
advocacy$response <- substring(row.names(advocacy), 1)
advocacy$response <- revalue(as.factor(advocacy$response), c("1" = "Yes",  "2" = "No"))

advocacy$plot <- factor(advocacy$response, advocacy$response)

cols <- c("#31a354","#f03b20", "grey") 
advocacy.plot <- ggplot(advocacy, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) 

advocacy.plot + geom_bar(aes(x = plot), data = advocacy, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

advocacy.tb <- as.factor(d$bike_advocacy)
advocacy.tb <- summary(advocacy.tb)
advocacy.tb <- as.data.frame(advocacy.tb)
advocacy.tb$Var1 <- substring(row.names(advocacy.tb), 1)
advocacy.tb$response <- revalue(as.factor(advocacy.tb$Var1), c("1" = "Yes", "2" = "No"))

plot.advocacy <- merge(advocacy, advocacy.tb, by = "response")
plot.advocacy <- plot.advocacy[-c(2, 4, 6)]
plot.advocacy <- setcolorder(plot.advocacy, c("response", "advocacy.tb", "Freq"))
plot.advocacy$order <- c(2, 1)
plot.advocacy <- plot.advocacy %>% arrange(order)
plot.advocacy <- plot.advocacy[-c(4)]
colnames(plot.advocacy) <- c("Response", "N", "Proportion")

kable(plot.advocacy) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 62 22.06
No 219 77.94

Q18: Do you ride with a recreational cycling group or club (e.g., Tripleshot, TriStars)?

group <- round(prop.table(table(factor(d$cycling_club, levels = c("1", "2"))))*100,2)
group <- as.data.frame(group)
group$response <- substring(row.names(group), 1)
group$response <- revalue(as.factor(group$response), c("1" = "Yes",  "2" = "No"))

group$plot <- factor(group$response, group$response)

group.plot <- ggplot(group, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) 

group.plot + geom_bar(aes(x = plot), data = group, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

group.tb <- as.factor(d$cycling_club)
group.tb <- summary(group.tb)
group.tb <- as.data.frame(group.tb)
group.tb$Var1 <- substring(row.names(group.tb), 1)

group.tb$response <- revalue(as.factor(group.tb$Var1), c("1" = "Yes", "2" = "No"))

plot.group <- merge(group, group.tb, by = "response")
plot.group <- plot.group[-c(2, 4, 6)]
plot.group <- setcolorder(plot.group, c("response", "group.tb", "Freq"))
plot.group$order <- c(2, 1)
plot.group <- plot.group %>% arrange(order)
plot.group <- plot.group[-c(4)]
colnames(plot.group) <- c("Response", "N", "Proportion")

kable(plot.group) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Yes 45 16.01
No 236 83.99

Section 2: Housing

Q19: Thinking about where you live now, are you:

tenure <- round(prop.table(table(factor(d$house_tenure, levels = c("1", "2", "3", "4", "5", "77"))))*100,2)
tenure <- as.data.frame(tenure)
tenure$response <- substring(row.names(tenure), 1)
tenure$response <- revalue(as.factor(tenure$response), c("1" = "An owner", "2" = "A tenant", "3" = "Resident in a relative or friend's home", "4" = "Resident other than in a relative or friend's home", "5" = "Other", "6" = "Don't know"))

tenure$plot <- factor(tenure$response, tenure$response)

tenure.plot <- ggplot(tenure, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=0.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteSet) +
      ylab("Percent of total") +
      xlab("")
      
tenure.plot + geom_bar(aes(x = plot), data = tenure, stat = "identity") 

tenure.tb <- as.factor(d$house_tenure)
tenure.tb <- summary(tenure.tb)
tenure.tb <- as.data.frame(tenure.tb)
tenure.tb$Var1 <- substring(row.names(tenure.tb), 1)

nval.df <- c("0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$tenure.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("4")
nval.df <- nval.df[-c(1)]

tenure.tb <- rbind(tenure.tb, nval.df)

tenure.tb$response <- revalue(as.factor(tenure.tb$Var1), c("1" = "An owner", "2" = "A tenant", "3" = "Resident in a relative or friend's home", "4" = "Resident other than in a relative or friend's home", "5" = "Other", "77" = "Don't know"))

plot.tenure <- merge(tenure, tenure.tb, by = "response")
plot.tenure <- plot.tenure[-c(2, 4, 6)]
plot.tenure <- setcolorder(plot.tenure, c("response", "tenure.tb", "Freq"))
plot.tenure$order <- c(2, 1, 6, 5, 3, 4)
plot.tenure <- plot.tenure %>% arrange(order)
plot.tenure <- plot.tenure[-c(4)]
colnames(plot.tenure) <- c("Response", "N", "Proportion")

kable(plot.tenure)  %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
An owner 175 62.28
A tenant 91 32.38
Resident other than in a relative or friend’s home 2 0.71
Resident other than in a relative or friend’s home 0 0.71
Resident in a relative or friend’s home 11 3.91
Other 2 0.71

Q20: In what type of dwelling to you currently live? Is it:

dwelling <- round(prop.table(table(factor(d$dwelling_type, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "10", "77"))))*100,2)
dwelling <- as.data.frame(dwelling)
dwelling$response <- substring(row.names(dwelling), 1)
dwelling$response <- revalue(as.factor(dwelling$response), c("1" = "Single detached house", "2" = "Semi-detached house", "3" = "Row house",  "4" = "An apartment (or condo) in a duplex or triplex", "5" = "Apartment (or condo) in building with fewer than 5 storeys", "6" = "Apartment (or condo) in building with more than 5 storeys", "7" = "Mobile home/movable dwelling", "8" = "Senior's home", "9" = "Other", "10" = "Don't know"))

dwelling$plot <- factor(dwelling$response, dwelling$response)

cols <- c("#08306b", "#08519c", "#2171b5", "#4292c6", "#6baed6", "#9ecae1", "#c6dbef", "#deebf7", "#f7fbff", "grey")
dwelling.plot <- ggplot(dwelling, aes(x = response, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
  scale_fill_manual (values = INTERACTPaletteSet) +
      ylab("Percent of total") +
      xlab("")
      
dwelling.plot + geom_bar(aes(x = plot), data = dwelling, stat = "identity") 

dwelling.tb <- as.factor(d$dwelling_type)
dwelling.tb <- summary(dwelling.tb)
dwelling.tb <- as.data.frame(dwelling.tb)
dwelling.tb$Var1 <- substring(row.names(dwelling.tb), 1)
                              
nval.df <- c("0", "0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$dwelling.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("8", "9")
nval.df <- nval.df[-c(1)]

dwelling.tb <- rbind(dwelling.tb, nval.df)

dwelling.tb$response <- revalue(as.factor(dwelling.tb$Var1), c("1" = "Single detached house", "2" = "Semi-detached house", "3" = "Row house",  "4" = "An apartment (or condo) in a duplex or triplex", "5" = "Apartment (or condo) in building with fewer than 5 storeys", "6" = "Apartment (or condo) in building with more than 5 storeys", "7" = "Mobile home/movable dwelling", "8" = "Senior's home", "10" = "Other", "77" = "Don't know"))
plot.dwelling <- merge(dwelling, dwelling.tb, by = "response")
plot.dwelling <- plot.dwelling[-c(4, 6)]
plot.dwelling <- setcolorder(plot.dwelling, c("response", "dwelling.tb", "Freq", "Var1.x"))
plot.dwelling <- plot.dwelling %>% arrange(Var1.x)
plot.dwelling <- plot.dwelling[-c(4)]
colnames(plot.dwelling) <- c("Response", "N", "Proportion")

kable(plot.dwelling)  %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Proportion
Single detached house 165 60.00
Semi-detached house 15 5.45
Row house 16 5.82
An apartment (or condo) in a duplex or triplex 15 5.45
Apartment (or condo) in building with fewer than 5 storeys 44 16.00
Apartment (or condo) in building with more than 5 storeys 19 6.91
Mobile home/movable dwelling 1 0.36
Senior’s home 0 0.00

Section 3a: Physical Activity

Q21: During the last 7 days, on how many days did you do vigorous physical activities like heavy lifting, digging, heavy construction, or climbing up stairs as part of your work? Think about only those physical activities that you did for at least 10 minutes at a time.

#work_vigpa

ggplot(d, aes(x = d$work_vigpa)) + geom_histogram(na.rm = TRUE, fill = "#1596FF") + xlab("N days vigorous job-related physical activity")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$work_vigpa)), Percentage = round(as.numeric(prop.table(table(d$work_vigpa)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Days N Percentage
0 210 74.73
1 16 5.69
2 8 2.85
3 11 3.91
4 10 3.56
5 14 4.98
6 4 1.42
7 8 2.85

Q22: How much time did you usually spend on one of those days doing vigorous physical activities as part of your work?

#work_vigpa_freq

d$work_vigpa_freq[d$work_vigpa_freq==-7] <- NA

ggplot(d, aes(x = d$work_vigpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes vigorous job-related physical activity") 

summary(d$work_vigpa_freq)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     0.0    60.0   120.0   152.8   240.0   480.0     211

Q23: During the last 7 days, on how many days did you travel in a motor vehicle like a train, bus, car, or metro?

#travel_motor

ggplot(d, aes(x = d$travel_motor)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$travel_motor)), Percentage = round(as.numeric(prop.table(table(d$travel_motor)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Days N Percentage
0 27 9.61
1 40 14.23
2 60 21.35
3 49 17.44
4 44 15.66
5 30 10.68
6 13 4.63
7 18 6.41

Q24: How much time did you usually spend on one of those days travelling in a train, bus, car, metro, or other kind of motor vehicle?

#travel_motor_freq

d$travel_motor_freq[d$travel_motor_freq==-7] <- NA
ggplot(d, aes(x = d$travel_motor_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") 

summary(d$travel_motor_freq)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00   30.00   40.00   55.69   60.00  900.00      29

Q25: During the last 7 days, on how many days did you bicycle for at least 10 minutes at a time to go from place to place?

#travel_bike

ggplot(d, aes(x = d$travel_bike)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$travel_bike)), Percentage = round(as.numeric(prop.table(table(d$travel_bike)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Days N Percentage
0 8 2.85
1 13 4.63
2 13 4.63
3 30 10.68
4 35 12.46
5 76 27.05
6 41 14.59
7 65 23.13

Q26: How much time did you usually spend on one of those days to bicycle from place to place?

#travel_bike_freq

d$travel_bike_freq[d$travel_bike_freq==-7] <- NA
ggplot(d, aes(x = d$travel_bike_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") 

summary(d$travel_bike_freq)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00   30.00   45.00   58.73   60.00  300.00      11

Q27: During the last 7 days, on how many days did you walk for at least 10 minutes at a time to go from place to place?

#travel_walk

ggplot(d, aes(x = d$travel_walk)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("# of days in the last 7 days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$travel_walk)), Percentage = round(as.numeric(prop.table(table(d$travel_walk)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Days N Percentage
0 40 14.23
1 29 10.32
2 40 14.23
3 43 15.30
4 25 8.90
5 31 11.03
6 12 4.27
7 61 21.71

Q28: How much time did you usually spend on one of those days walking from place to place?

#travel_walk_freq

d$travel_walk_freq[d$travel_walk_freq==-7] <- NA

ggplot(d, aes(x = d$travel_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") 

summary(d$travel_walk_freq)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    10.0    20.0    30.0    41.4    60.0   300.0      42

Section 3b: Physical activity from recreation, sport and leisure

Q29: Not counting any walking for transportation that you have already mentioned, during the last 7 days, on how many days did you walk for at least 10 minutes at a time in your leisure time?

#leisure_walk

ggplot(d, aes(x = d$leisure_walk)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$leisure_walk)), Percentage = round(as.numeric(prop.table(table(d$leisure_walk))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Days N Percentage
0 59 21
1 43 15
2 56 20
3 31 11
4 18 6
5 21 7
6 6 2
7 47 17

##Q30: How much time did you usually spend on one of those days walking in your leisure time?

#leisure_walk_freq

d$leisure_walk_freq[d$leisure_walk_freq==-7] <- NA

ggplot(d, aes(x = d$leisure_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") 

summary(d$leisure_walk_freq)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    10.0    30.0    45.0    64.3    60.0   900.0      61

Think about only those physical activities that you did for at least 10 minutes at a time, not counting any activity for transportation or work that you have already mentioned.

Q31: During the last 7 days, on how many days did you do vigorous physical activities like aerobics, running, fast bicycling, or fast swimming in your leisure time?

#leisure_vigpa

ggplot(d, aes(x = d$leisure_vigpa)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$leisure_vigpa)), Percentage = round(as.numeric(prop.table(table(d$leisure_vigpa))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Days N Percentage
0 86 31
1 30 11
2 48 17
3 46 16
4 32 11
5 17 6
6 12 4
7 10 4

Q32: How much time did you usually spend on one of those days doing vigorous physical activities in your leisure time?

#leisure_vigpa_freq

d$leisure_vigpa_freq[d$leisure_vigpa_freq==-7] <- NA

ggplot(d, aes(x = d$leisure_vigpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") 

summary(d$leisure_vigpa_freq)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00   45.00   60.00   77.05  120.00  300.00      87

Q33: During the last 7 days, on how many days did you do moderate physical activities like bicycling at a regular pace, swimming at a regular pace, or doubles tennis in your leisure time?

#leisure_modpa

ggplot(d, aes(x = d$leisure_modpa)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")

kable(data.frame(Days = 0:7, N = as.numeric(table(d$leisure_modpa)), Percentage = round(as.numeric(prop.table(table(d$leisure_modpa))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Days N Percentage
0 108 38
1 49 17
2 39 14
3 30 11
4 11 4
5 16 6
6 13 5
7 15 5

Q34: How much time did you usually spend on one of those days doing moderate physical activities in your leisure time?

#leisure_modpa_freq

d$leisure_modpa_freq[d$leisure_modpa_freq==-7] <- NA

ggplot(d, aes(x = d$leisure_modpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") 

summary(d$leisure_modpa_freq)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   30.00   60.00   68.62   60.00  360.00     111

Q35: During the last 7 days, how much time did you usually spend sitting on a weekday?

#sit_weekday

ggplot(d, aes(x = d$sit_weekday/60)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill= "#35AAC2") + xlab("Hours sitting, weekday") 

summary(d$sit_weekday)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    20.0   240.0   360.0   376.2   540.0   840.0       3

Q36: During the last 7 days, how much time did you usually spend sitting on a weekend day?

#sit_weekend

ggplot(d, aes(x = d$sit_weekend/60)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill= "#35AAC2") + xlab("Hours sitting, weekend") 

summary(d$sit_weekend)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     2.0   120.0   240.0   229.3   300.0   840.0       2

Section 4: General Health

Q37: How tall are you?

#height
#exclude outliers?

ggplot(d, aes(x = d$height)) + geom_histogram(na.rm = TRUE, binwidth = 2, fill="#76D24A") + xlab("Height (cm)") 

summary(d$height)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   155.0   165.0   170.0   172.2   178.0   201.0

Q38: How much do you weigh?

#weight

ggplot(d, aes(x = d$weight)) + geom_histogram(na.rm = TRUE, binwidth = 2, fill="#76D24A") + xlab("Weight (kg)") 

summary(d$weight)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    45.0    61.0    70.0    71.7    80.0   150.0

Q39: In general, would you say your health is:

#sf1
# Create proportional table
sf1 <- round(prop.table(table(d$sf1))*100,2)
sf1 <- as.data.frame(sf1)
sf1$group <- substring(rownames(sf1), 1)
# or use colnames(sf1)[1] <- "group" : 

# Change category values and transform in factor
## as.character(sf1$group) is because as.data.frame transform character into factor
sf1$group <- revalue(as.character(sf1$group), c("1" = "Excellent", "2" = "Very good", "3" = "Good", "4" = "Fair", "5" = "Poor"))

# Create plot
sf1$plot <- factor(sf1$group, sf1$group) ## Necessary to order x-axis in ggplot

sf1.plot <- ggplot(sf1, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPalette3) +
      ylab("Percent of total") +
      xlab("")
      
sf1.plot + geom_histogram(aes(x = plot), data = sf1, stat = "identity")

# make a clean summary table 
## make a dataframe on count 
sf1.tb <- as.factor(d$sf1)
sf1.tb <- summary(sf1.tb)
sf1.tb <- as.data.frame(sf1.tb)
sf1.tb$Var1 <- substring(row.names(sf1.tb), 1)
sf1.tb$group <- revalue(as.character(sf1.tb$Var1), c("1" = "Excellent", "2" = "Very good", "3" = "Good", "4" = "Fair", "5" = "Poor"))

## merge with existing prop table data used for plot above 
## order doesn't work
plot.sf1.tb <- merge(sf1, sf1.tb, by = "group")
plot.sf1.tb <- plot.sf1.tb[-c(2, 4)]
plot.sf1.tb <- setcolorder(plot.sf1.tb, c("group", "sf1.tb", "Freq", "Var1.y"))
plot.sf1.tb <- plot.sf1.tb %>% arrange(Var1.y)
plot.sf1.tb <- plot.sf1.tb[-c(4)]
colnames(plot.sf1.tb) <- c("Response", "N", "Percentage")

kable(plot.sf1.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Excellent 69 24.56
Very good 138 49.11
Good 60 21.35
Fair 13 4.63
Poor 1 0.36

Q40: The following questions are about activities you might do during a typical day. Does your health now limit you in these activities? If so, how much?

a. Moderate activities such as moving a table, pushing a vacuum cleaner, bowling, or playing golf

sf2 <- round(prop.table(table(factor(d$sf2, levels = c("1", "2", "3")), exclude = NULL))*100,2)
sf2 <- as.data.frame(sf2)
sf2$group <- substring(row.names(sf2), 1)
sf2$group <- revalue(as.character(sf2$group), c("1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all"))

sf2$plot <- factor(sf2$group, sf2$group)

sf2.plot <- ggplot(sf2, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshorterfade) +
      ylab("Percent of total") +
      xlab("")
sf2.plot + geom_histogram(aes(x = plot), data = sf2, stat = "identity")

sf2.tb <- as.factor(d$sf2)
sf2.tb <- summary(sf2.tb)
sf2.tb <- as.data.frame(sf2.tb)
sf2.tb$Var1 <- substring(row.names(sf2.tb), 1)
sf2.tb$group <- revalue(as.character(sf2.tb$Var1), c("1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all"))
plot.sf2.tb <- merge(sf2, sf2.tb, by = "group")
plot.sf2.tb <- plot.sf2.tb[-c(2, 4, 6)]
plot.sf2.tb <- setcolorder(plot.sf2.tb, c("group", "sf2.tb", "Freq"))
plot.sf2.tb$order <- c(3, 2, 1)
plot.sf2.tb <- plot.sf2.tb %>% arrange(order)
plot.sf2.tb <- plot.sf2.tb[-c(4)]
colnames(plot.sf2.tb) <- c("Response", "N", "Percentage")

kable(plot.sf2.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Yes, limited a lot 3 1.07
Yes, limited a little 20 7.12
No, not at all 258 91.81

b. Climbing several flights of stairs

# sf3 

sf3 <- round(prop.table(table(factor(d$sf3, levels = c("1", "2", "3")), exclude = NULL))*100,2)
sf3 <- as.data.frame(sf3)
sf3$group <- substring(row.names(sf3), 1)
sf3$group <- revalue(as.character(sf3$group), c("1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all"))

sf3$plot <- factor(sf3$group, sf3$group)

sf3.plot <- ggplot(sf3, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshorterfade) +
      ylab("Percent of total") +
      xlab("")
      
sf3.plot + geom_histogram(aes(x = plot), data = sf3, stat = "identity") 

# summary table

sf3.tb <- as.factor(d$sf3)
sf3.tb <- summary(sf3.tb)
sf3.tb <- as.data.frame(sf3.tb)
sf3.tb$Var1 <- substring(row.names(sf3.tb), 1)
sf3.tb$group <- revalue(as.character(sf3.tb$Var1), c("1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all"))
plot.sf3.tb <- merge(sf3, sf3.tb, by = "group")
plot.sf3.tb <- plot.sf3.tb[-c(2, 4, 6)]
plot.sf3.tb <- setcolorder(plot.sf3.tb, c("group", "sf3.tb", "Freq"))
plot.sf3.tb$order <- c(3, 2, 1)
plot.sf3.tb <- plot.sf3.tb %>% arrange(order)
plot.sf3.tb <- plot.sf3.tb[-c(4)]
colnames(plot.sf3.tb) <- c("Response", "N", "Percentage")

kable(plot.sf3.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Yes, limited a lot 5 1.78
Yes, limited a little 20 7.12
No, not at all 256 91.10

Q41: During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of your physical health?

a. Accomplished less than you would like

#sf4 

sf4<- round(prop.table(table(factor(d$sf4, levels = c("1", "2")), exclude = NULL))*100,2)
sf4 <- as.data.frame(sf4)
sf4$group <- substring(row.names(sf4), 1)
sf4$group <- revalue(as.character(sf4$group), c("1" = "Yes",  "2" = "No"))

sf4$plot <- factor(sf4$group, sf4$group)

sf4.plot <- ggplot(sf4, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

sf4.plot + geom_histogram(aes(x = plot), data = sf4, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

sf4.tb <- as.factor(d$sf4)
sf4.tb <- summary(sf4.tb)
sf4.tb <- as.data.frame(sf4.tb)
sf4.tb$Var1 <- substring(row.names(sf4.tb), 1)
sf4.tb$group <- revalue(as.character(sf4.tb$Var1), c("1" = "Yes", "2" = "No"))
plot.sf4.tb <- merge(sf4, sf4.tb, by = "group")
plot.sf4.tb <- plot.sf4.tb[-c(2, 4, 6)]
plot.sf4.tb <- setcolorder(plot.sf4.tb, c("group", "sf4.tb", "Freq"))
plot.sf4.tb$order <- c(2, 1)
plot.sf4.tb <- plot.sf4.tb %>% arrange(order)
plot.sf4.tb <- plot.sf4.tb[-c(4)]
colnames(plot.sf4.tb) <- c("Response", "N", "Percentage")

kable(plot.sf4.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Yes 58 20.64
No 223 79.36

b. Were limited in the kind of work or other activities

#sf5
sf5<- round(prop.table(table(factor(d$sf5, levels = c("1", "2")), exclude = NULL))*100,2)
sf5 <- as.data.frame(sf5)
sf5$group <- substring(row.names(sf5), 1)
sf5$group <- revalue(as.character(sf5$group), c("1" = "Yes",  "2" = "No"))

sf5$plot <- factor(sf5$group, sf5$group)

sf5.plot <- ggplot(sf5, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

sf5.plot + geom_histogram(aes(x = plot), data = sf5, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

sf5.tb <- as.factor(d$sf5)
sf5.tb <- summary(sf5.tb)
sf5.tb <- as.data.frame(sf5.tb)
sf5.tb$Var1 <- substring(row.names(sf5.tb), 1)
sf5.tb$group <- revalue(as.character(sf5.tb$Var1), c("1" = "Yes", "2" = "No"))
plot.sf5.tb <- merge(sf5, sf5.tb, by = "group")
plot.sf5.tb <- plot.sf5.tb[-c(2, 4, 6)]
plot.sf5.tb <- setcolorder(plot.sf5.tb, c("group", "sf5.tb", "Freq"))
plot.sf5.tb$order <- c(2, 1)
plot.sf5.tb <- plot.sf5.tb %>% arrange(order)
plot.sf5.tb <- plot.sf5.tb[-c(4)]
colnames(plot.sf5.tb) <- c("Response", "N", "Percentage")

kable(plot.sf5.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Yes 47 16.73
No 234 83.27

Q42: During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)?

a. Accomplished less than you would like

#sf6

sf6<- round(prop.table(table(factor(d$sf6, levels = c("1", "2")), exclude = NULL))*100,2)
sf6 <- as.data.frame(sf6)
sf6$group <- substring(row.names(sf6), 1)
sf6$group <- revalue(as.character(sf6$group), c("1" = "Yes",  "2" = "No"))

sf6$plot <- factor(sf6$group, sf6$group)

sf6.plot <- ggplot(sf6, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

sf6.plot + geom_histogram(aes(x = plot), data = sf6, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

# summary table

sf6.tb <- as.factor(d$sf6)
sf6.tb <- summary(sf6.tb)
sf6.tb <- as.data.frame(sf6.tb)
sf6.tb$Var1 <- substring(row.names(sf6.tb), 1)
sf6.tb$group <- revalue(as.character(sf6.tb$Var1), c("1" = "Yes", "2" = "No"))
plot.sf6.tb <- merge(sf6, sf6.tb, by = "group")
plot.sf6.tb <- plot.sf6.tb[-c(2, 4, 6)]
plot.sf6.tb <- setcolorder(plot.sf6.tb, c("group", "sf6.tb", "Freq"))
plot.sf6.tb$order <- c(2, 1)
plot.sf6.tb <- plot.sf6.tb %>% arrange(order)
plot.sf6.tb <- plot.sf6.tb[-c(4)]
colnames(plot.sf6.tb) <- c("Response", "N", "Percentage")

kable(plot.sf6.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Yes 64 22.78
No 217 77.22

b. Did work or activities less carefully than usual

#sf7

sf7<- round(prop.table(table(factor(d$sf7, levels = c("1", "2")), exclude = NULL))*100,2)
sf7 <- as.data.frame(sf7)
sf7$group <- substring(row.names(sf7), 1)
sf7$group <- revalue(as.character(sf7$group), c("1" = "Yes",  "2" = "No"))

sf7$plot <- factor(sf7$group, sf7$group)

sf7.plot <- ggplot(sf7, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

sf7.plot + geom_histogram(aes(x = plot), data = sf7, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

sf7.tb <- as.factor(d$sf7)
sf7.tb <- summary(sf7.tb)
sf7.tb <- as.data.frame(sf7.tb)
sf7.tb$Var1 <- substring(row.names(sf7.tb), 1)
sf7.tb$group <- revalue(as.character(sf7.tb$Var1), c("1" = "Yes", "2" = "No"))
plot.sf7.tb <- merge(sf7, sf7.tb, by = "group")
plot.sf7.tb <- plot.sf7.tb[-c(2, 4, 6)]
plot.sf7.tb <- setcolorder(plot.sf7.tb, c("group", "sf7.tb", "Freq"))
plot.sf7.tb$order <- c(2, 1)
plot.sf7.tb <- plot.sf7.tb %>% arrange(order)
plot.sf7.tb <- plot.sf7.tb[-c(4)]
colnames(plot.sf7.tb) <- c("Response", "N", "Percentage")

kable(plot.sf7.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Yes 52 18.51
No 229 81.49

Q43: During the past 4 weeks, how much did pain interfere with your normal work (including work outside the home and housework)?

#sf8

sf8 <- round(prop.table(table(factor(d$sf8, levels = c("1", "2", "3", "4", "5")), exclude = NULL))*100,2)
sf8 <- as.data.frame(sf8)
sf8$group <- substring(row.names(sf8), 1)
sf8$group <- revalue(as.character(sf8$group), c("1" = "Not at all",  "2" = "Slightly", "3" = "Moderately", "4" =  "Quite a bit", "5" = "Extremely"))

sf8$plot <- factor(sf8$group, sf8$group)

sf8.plot <- ggplot(sf8, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=rev(INTERACTshortfade)) +
      ylab("Percent of total") +
      xlab("")
      
sf8.plot + geom_histogram(aes(x = plot), data = sf8, stat = "identity") 

sf8.tb <- as.factor(d$sf8)
sf8.tb <- summary(sf8.tb)
sf8.tb <- as.data.frame(sf8.tb)
sf8.tb$Var1 <- substring(row.names(sf8.tb), 1)
sf8.tb$group <- revalue(as.character(sf8.tb$Var1), c("1" = "Not at all",  "2" = "Slightly", "3" = "Moderately", "4" =  "Quite a bit", "5" = "Extremely"))
plot.sf8 <- merge(sf8, sf8.tb, by = "group")
plot.sf8 <- plot.sf8[-c(2, 4, 6)]
plot.sf8 <- setcolorder(plot.sf8, c("group", "sf8.tb", "Freq"))
plot.sf8$order <- c(5,3,1,4,2)
plot.sf8 <- plot.sf8 %>% arrange(order)
plot.sf8 <- plot.sf8[-c(4)]
colnames(plot.sf8) <- c("Response", "N", "Percentage")

kable(plot.sf8) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Not at all 176 62.63
Slightly 75 26.69
Moderately 18 6.41
Quite a bit 9 3.20
Extremely 3 1.07

Q44: How much of the time during the past 4 weeks.

a. Have you felt calm and peaceful?

#sf9 

sf9 <- round(prop.table(table(factor(d$sf9, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100,2)
sf9 <- as.data.frame(sf9)
sf9$group <- substring(row.names(sf9), 1)
sf9$group <- revalue(as.character(sf9$group), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))

sf9$plot <- factor(sf9$group, sf9$group)

sf9.plot <- ggplot(sf9, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
sf9.plot + geom_histogram(aes(x = plot), data = sf9, stat = "identity") 

sf9.tb <- as.factor(d$sf9)
sf9.tb <- summary(sf9.tb)
sf9.tb <- as.data.frame(sf9.tb)
sf9.tb$Var1 <- substring(row.names(sf9.tb), 1)

sf9.tb$group <- revalue(as.character(sf9.tb$Var1), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))
plot.sf9 <- merge(sf9, sf9.tb, by = "group")
plot.sf9 <- plot.sf9[-c(4, 6)]
plot.sf9 <- setcolorder(plot.sf9, c("group", "sf9.tb", "Freq", "Var1.x"))
plot.sf9 <- plot.sf9 %>% arrange(Var1.x)
plot.sf9 <- plot.sf9[-c(4)]
colnames(plot.sf9) <- c("Response", "N", "Percentage")

kable(plot.sf9) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
All of the time 6 2.14
Most of the time 103 36.65
A good bit of the time 94 33.45
Some of the time 59 21.00
A little of the time 18 6.41
None of the time 1 0.36

b. Did you have a lot of energy?

sf10 <- round(prop.table(table(factor(d$sf10, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100,2)
sf10 <- as.data.frame(sf10)
sf10$group <- substring(row.names(sf10), 1)
sf10$group <- revalue(as.character(sf10$group), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))

sf10$plot <- factor(sf10$group, sf10$group)

sf10.plot <- ggplot(sf10, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTfade) +
      ylab("Percent of total") +
      xlab("")
      
sf10.plot + geom_histogram(aes(x = plot), data = sf10, stat = "identity") 

sf10.tb <- as.factor(d$sf10)
sf10.tb <- summary(sf10.tb)
sf10.tb <- as.data.frame(sf10.tb)
sf10.tb$Var1 <- substring(row.names(sf10.tb), 1)
sf10.tb$group <- revalue(as.character(sf10.tb$Var1), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))
plot.sf10 <- merge(sf10, sf10.tb, by = "group")
plot.sf10 <- plot.sf10[-c(2, 4, 6)]
plot.sf10 <- setcolorder(plot.sf10, c("group", "sf10.tb", "Freq"))
plot.sf10$order <- c(3,5,1,2,6,4)
plot.sf10 <- plot.sf10 %>% arrange(order)
plot.sf10 <- plot.sf10[-c(4)]
colnames(plot.sf10) <- c("Response", "N", "Percentage")

kable(plot.sf10) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
All of the time 10 3.56
Most of the time 137 48.75
A good bit of the time 69 24.56
Some of the time 47 16.73
A little of the time 15 5.34
None of the time 2 0.71

c. Have you felt downhearted and blue?

#check all of the time is 0 

#sf11

sf11 <- round(prop.table(table(factor(d$sf11, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100,2)
sf11 <- as.data.frame(sf11)
sf11$group <- substring(row.names(sf11), 1)
sf11$group <- revalue(as.character(sf11$group), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))

sf11$plot <- factor(sf11$group, sf11$group)

sf11.plot <- ggplot(sf11, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTfade) +
      ylab("Percent of total") +
      xlab("")
      
sf11.plot + geom_histogram(aes(x = plot), data = sf11, stat = "identity") 

sf11.tb <- as.factor(d$sf11)
sf11.tb <- summary(sf11.tb)
sf11.tb <- as.data.frame(sf11.tb)
sf11.tb$Var1 <- substring(row.names(sf11.tb), 1)

nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$sf11.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("1")
nval.df <- nval.df[-c(1)]

sf11.tb <- rbind(sf11.tb, nval.df)

sf11.tb$group <- revalue(as.character(sf11.tb$Var1), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))
plot.sf11 <- merge(sf11, sf11.tb, by = "group")
plot.sf11 <- plot.sf11[-c(2, 4)]
plot.sf11 <- setcolorder(plot.sf11, c("group", "sf11.tb", "Freq", "Var1.y"))
plot.sf11 <- plot.sf11 %>% arrange(Var1.y)
plot.sf11 <- plot.sf11[-c(4)]
colnames(plot.sf11) <- c("Response", "N", "Percentage")

kable(plot.sf11) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
All of the time 0 0.00
Most of the time 2 0.71
A good bit of the time 14 4.98
Some of the time 59 21.00
A little of the time 137 48.75
None of the time 68 24.20

Q45: During the past 4 weeks, how much of the time has your physical health or emotional problems interfered with your social activities (like visiting friends, relatives, etc.)?

#sf12

sf12 <- round(prop.table(table(factor(d$sf12, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100,2)
sf12 <- as.data.frame(sf12)
sf12$group <- substring(row.names(sf12), 1)
sf12$group <- revalue(as.character(sf12$group), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))

sf12$plot <- factor(sf12$group, sf12$group)

sf12.plot <- ggplot(sf12, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
sf12.plot + geom_histogram(aes(x = plot), data = sf12, stat = "identity") 

sf12.tb <- as.factor(d$sf12)
sf12.tb <- summary(sf12.tb)
sf12.tb <- as.data.frame(sf12.tb)
sf12.tb$Var1 <- substring(row.names(sf12.tb), 1)
sf12.tb$group <- revalue(as.character(sf12.tb$Var1), c("1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time"))
plot.sf12 <- merge(sf12, sf12.tb, by = "group")
plot.sf12 <- plot.sf12[-c(2, 4, 6)]
plot.sf12 <- setcolorder(plot.sf12, c("group", "sf12.tb", "Freq"))
plot.sf12$order <- c(3,5,1,2,4)
plot.sf12 <- plot.sf12 %>% arrange(order)
plot.sf12 <- plot.sf12[-c(4)]
colnames(plot.sf12) <- c("Response", "N", "Percentage")

kable(plot.sf12) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
All of the time 2 0.71
None of the time 176 62.63
A good bit of the time 12 4.27
Some of the time 23 8.19
A little of the time 68 24.20

Section 5: Personal Well-being

These questions need to be redone.

Q46: Thinking about your own life and personal circumstances, how satisfied are you.

a. With your life as a whole?

d$pwb_a
## check attention n=0 for 10

pwb_vic_a <- round(prop.table(table(factor(d$pwb_vic_a, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100,2)


pwb_vic_a <- as.data.frame(pwb_vic_a)
pwb_vic_a$group <- substring(row.names(pwb_vic_a), 1)
pwb_vic_a$group <- revalue(as.character(pwb_vic_a$group), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

pwb_vic_a$plot <- factor(pwb_vic_a$group, pwb_vic_a$group)
pwb_vic_a.plot <- ggplot(pwb_vic_a, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

View(d$pwb_vic_a)
      
pwb_vic_a.plot + geom_histogram(aes(x = plot), data = pwb_vic_a, stat = "identity") 

pwb_vic_a.tb <- as.factor(d$pwb_vic_a)
pwb_vic_a.tb <- summary(pwb_vic_a.tb)
pwb_vic_a.tb <- as.data.frame(pwb_vic_a.tb)
pwb_vic_a.tb$Var1 <- substring(row.names(pwb_vic_a.tb), 1)

nval.df <- c("0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$pwb_vic_a.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("10")
nval.df <- nval.df[-c(1)]

pwb_vic_a.tb <- rbind(pwb_vic_a.tb, nval.df)
                           
pwb_vic_a.tb$group <- revalue(as.character(pwb_vic_a.tb$Var1), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

plot.pwb_vic_a <- merge(pwb_vic_a, pwb_vic_a.tb, by = "group")
plot.pwb_vic_a <- plot.pwb_vic_a[-c(2, 4, 6)]
plot.pwb_vic_a <- setcolorder(plot.pwb_vic_a, c("group", "pwb_vic_a.tb", "Freq"))
plot.pwb_vic_a$order <- c(1, 10, 2, 3, 4, 5, 6, 7, 8, 9)
plot.pwb_vic_a <- plot.pwb_vic_a %>% arrange(order)
plot.pwb_vic_a <- plot.pwb_vic_a[-c(4)]
colnames(plot.pwb_vic_a) <- c("Response", "N", "Percentage")

kable(plot.pwb_vic_a) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

b. With your standard of living?


#pwb_vic_b

pwb_vic_b <- round(prop.table(table(factor(d$pwb_vic_b, levels= c(1:10))))*100,2)

pwb_vic_b <- as.data.frame(pwb_vic_b)
pwb_vic_b$group <- substring(row.names(pwb_vic_b), 1)
pwb_vic_b$group <- revalue(as.character(pwb_vic_b$group), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

pwb_vic_b$plot <- factor(pwb_vic_b$group, pwb_vic_b$group)
pwb_vic_b.plot <- ggplot(pwb_vic_b, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

      
pwb_vic_b.plot + geom_histogram(aes(x = plot), data = pwb_vic_b, stat = "identity") 

pwb_vic_b.tb <- as.factor(d$pwb_vic_b)
pwb_vic_b.tb <- summary(pwb_vic_b.tb)
pwb_vic_b.tb <- as.data.frame(pwb_vic_b.tb)
pwb_vic_b.tb$Var1 <- substring(row.names(pwb_vic_b.tb), 1)


nval.df <- c("0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$pwb_vic_b.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("10")
nval.df <- nval.df[-c(1)]

pwb_vic_b.tb <- rbind(pwb_vic_b.tb, nval.df)
                           
pwb_vic_b.tb$group <- revalue(as.character(pwb_vic_b.tb$Var1), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

plot.pwb_vic_b <- merge(pwb_vic_b, pwb_vic_b.tb, by = "group")
plot.pwb_vic_b <- plot.pwb_vic_b[-c(4, 6)]
plot.pwb_vic_b <- setcolorder(plot.pwb_vic_b, c("group", "pwb_vic_b.tb", "Freq", "Var1.x"))
plot.pwb_vic_b <- plot.pwb_vic_b %>% arrange(Var1.x)
plot.pwb_vic_b <- plot.pwb_vic_b[-c(4)]
colnames(plot.pwb_vic_b) <- c("Response", "N", "Percentage")

kable(plot.pwb_vic_b) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

c. With your health?

#pwb_vic_c

pwb_vic_c <- round(prop.table(table(factor(d$pwb_vic_c, levels= c(1:10))))*100,2)

pwb_vic_c <- as.data.frame(pwb_vic_c)
pwb_vic_c$group <- substring(row.names(pwb_vic_c), 1)
pwb_vic_c$group <- revalue(as.character(pwb_vic_c$group), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

pwb_vic_c$plot <- factor(pwb_vic_c$group, pwb_vic_c$group)
pwb_vic_c.plot <- ggplot(pwb_vic_c, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

      
pwb_vic_c.plot + geom_histogram(aes(x = plot), data = pwb_vic_c, stat = "identity") 

pwb_vic_c.tb <- as.factor(d$pwb_vic_c)
pwb_vic_c.tb <- summary(pwb_vic_c.tb)
pwb_vic_c.tb <- as.data.frame(pwb_vic_c.tb)
pwb_vic_c.tb$Var1 <- substring(row.names(pwb_vic_c.tb), 1)

nval.df <- c("0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$pwb_vic_c.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("10")
nval.df <- nval.df[-c(1)]

pwb_vic_c.tb <- rbind(pwb_vic_c.tb, nval.df)
                           
pwb_vic_c.tb$group <- revalue(as.character(pwb_vic_c.tb$Var1), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

plot.pwb_vic_c <- merge(pwb_vic_c, pwb_vic_c.tb, by = "group")
plot.pwb_vic_c <- plot.pwb_vic_c[-c(2, 4, 6)]
plot.pwb_vic_c <- setcolorder(plot.pwb_vic_c, c("group", "pwb_vic_c.tb", "Freq"))
plot.pwb_vic_c$order <- c(1, 10, 2, 3, 4, 5, 6, 7, 8, 9)
plot.pwb_vic_c <- plot.pwb_vic_c %>% arrange(order)
plot.pwb_vic_c <- plot.pwb_vic_c[-c(4)]
colnames(plot.pwb_vic_c) <- c("Response", "N", "Percentage")

kable(plot.pwb_vic_c) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

d. With what you are achieving in life?

#pwb_vic_d

pwb_vic_d <- round(prop.table(table(factor(d$pwb_vic_d, levels=c(1:10))))*100,2)

pwb_vic_d <- as.data.frame(pwb_vic_d)
pwb_vic_d$group <- substring(row.names(pwb_vic_d), 1)
pwb_vic_d$group <- revalue(as.character(pwb_vic_d$group), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

pwb_vic_d$plot <- factor(pwb_vic_d$group, pwb_vic_d$group)
pwb_vic_d.plot <- ggplot(pwb_vic_d, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

      
pwb_vic_d.plot + geom_histogram(aes(x = plot), data = pwb_vic_d, stat = "identity") 

pwb_vic_d.tb <- as.factor(d$pwb_vic_d)
pwb_vic_d.tb <- summary(pwb_vic_d.tb)
pwb_vic_d.tb <- as.data.frame(pwb_vic_d.tb)
pwb_vic_d.tb$Var1 <- substring(row.names(pwb_vic_d.tb), 1)
                           
pwb_vic_d.tb$group <- revalue(as.character(pwb_vic_d.tb$Var1), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

plot.pwb_vic_d <- merge(pwb_vic_d, pwb_vic_d.tb, by = "group")
plot.pwb_vic_d <- plot.pwb_vic_d[-c(4, 6)]
plot.pwb_vic_d <- setcolorder(plot.pwb_vic_d, c("group", "pwb_vic_d.tb", "Freq", "Var1.x"))
plot.pwb_vic_d <- plot.pwb_vic_d %>% arrange(Var1.x)
plot.pwb_vic_d <- plot.pwb_vic_d[-c(4)]
colnames(plot.pwb_vic_d) <- c("Response", "N", "Percentage")

kable(plot.pwb_vic_d) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

e. With your personal relationships?

#pwb_vic_e

pwb_vic_e <- round(prop.table(table(factor(d$pwb_vic_e, levels=c(1:10))))*100,2)

pwb_vic_e <- as.data.frame(pwb_vic_e)
pwb_vic_e$group <- substring(row.names(pwb_vic_e), 1)
pwb_vic_e$group <- revalue(as.character(pwb_vic_e$group), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

pwb_vic_e$plot <- factor(pwb_vic_e$group, pwb_vic_e$group)
pwb_vic_e.plot <- ggplot(pwb_vic_e, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

      
pwb_vic_e.plot + geom_histogram(aes(x = plot), data = pwb_vic_e, stat = "identity") 

pwb_vic_e.tb <- as.factor(d$pwb_vic_e)
pwb_vic_e.tb <- summary(pwb_vic_e.tb)
pwb_vic_e.tb <- as.data.frame(pwb_vic_e.tb)
pwb_vic_e.tb$Var1 <- substring(row.names(pwb_vic_e.tb), 1)

pwb_vic_e.tb$group <- revalue(as.character(pwb_vic_e.tb$Var1), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

plot.pwb_vic_e <- merge(pwb_vic_e, pwb_vic_e.tb, by = "group")
plot.pwb_vic_e <- plot.pwb_vic_e[-c(2, 4, 6)]
plot.pwb_vic_e <- setcolorder(plot.pwb_vic_e, c("group", "pwb_vic_e.tb", "Freq"))
plot.pwb_vic_e$order <- c(1, 10, 2, 3, 4, 5, 6, 7, 8, 9)
plot.pwb_vic_e <- plot.pwb_vic_e %>% arrange(order)
plot.pwb_vic_e <- plot.pwb_vic_e[-c(4)]
colnames(plot.pwb_vic_e) <- c("Response", "N", "Percentage")

kable(plot.pwb_vic_e) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

f. With how safe you feel?

#pwb_vic_f

pwb_vic_f <- round(prop.table(table(factor(d$pwb_vic_f, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100,2)

pwb_vic_f <- as.data.frame(pwb_vic_f)
pwb_vic_f$group <- substring(row.names(pwb_vic_f), 1)
pwb_vic_f$group <- revalue(as.character(pwb_vic_f$group), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

pwb_vic_f$plot <- factor(pwb_vic_f$group, pwb_vic_f$group)
pwb_vic_f.plot <- ggplot(pwb_vic_f, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

      
pwb_vic_f.plot + geom_histogram(aes(x = plot), data = pwb_vic_f, stat = "identity") 



pwb_vic_f.tb <- as.factor(d$pwb_vic_f)
pwb_vic_f.tb <- summary(pwb_vic_f.tb)
pwb_vic_f.tb <- as.data.frame(pwb_vic_f.tb)
pwb_vic_f.tb$Var1 <- substring(row.names(pwb_vic_f.tb), 1)

nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$pwb_vic_f.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("7")
nval.df <- nval.df[-c(1)]

pwb_vic_f.tb <- rbind(pwb_vic_f.tb, nval.df)

                           
pwb_vic_f.tb$group <- revalue(as.character(pwb_vic_f.tb$Var1), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

plot.pwb_vic_f <- merge(pwb_vic_f, pwb_vic_f.tb, by = "group")
plot.pwb_vic_f <- plot.pwb_vic_f[-c(4, 6)]
plot.pwb_vic_f <- setcolorder(plot.pwb_vic_f, c("group", "pwb_vic_f.tb", "Freq", "Var1.x"))
plot.pwb_vic_f <- plot.pwb_vic_f %>% arrange(Var1.x)
plot.pwb_vic_f <- plot.pwb_vic_f[-c(4)]
colnames(plot.pwb_vic_f) <- c("Response", "N", "Percentage")

kable(plot.pwb_vic_f) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

g. With feeling part of your community?

#pwb_vic_g
pwb_vic_g <- round(prop.table(table(factor(d$pwb_vic_g, levels=c(1:10))))*100,2)

pwb_vic_g <- as.data.frame(pwb_vic_g)
pwb_vic_g$group <- substring(row.names(pwb_vic_g), 1)
pwb_vic_g$group <- revalue(as.character(pwb_vic_g$group), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

pwb_vic_g$plot <- factor(pwb_vic_g$group, pwb_vic_g$group)
pwb_vic_g.plot <- ggplot(pwb_vic_g, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

      
pwb_vic_g.plot + geom_histogram(aes(x = plot), data = pwb_vic_g, stat = "identity") 

pwb_vic_g.tb <- as.factor(d$pwb_vic_g)
pwb_vic_g.tb <- summary(pwb_vic_g.tb)
pwb_vic_g.tb <- as.data.frame(pwb_vic_g.tb)
pwb_vic_g.tb$Var1 <- substring(row.names(pwb_vic_g.tb), 1)
                           
pwb_vic_g.tb$group <- revalue(as.character(pwb_vic_g.tb$Var1), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

plot.pwb_vic_g <- merge(pwb_vic_g, pwb_vic_g.tb, by = "group")
plot.pwb_vic_g <- plot.pwb_vic_g[-c( 4, 6)]
plot.pwb_vic_g <- setcolorder(plot.pwb_vic_g, c("group", "pwb_vic_g.tb", "Freq", "Var1.x"))
plot.pwb_vic_g <- plot.pwb_vic_g %>% arrange(Var1.x)
plot.pwb_vic_g <- plot.pwb_vic_g[-c(4)]
colnames(plot.pwb_vic_g) <- c("Response", "N", "Percentage")

kable(plot.pwb_vic_g) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

h. With your future security?

#pwb_vic_h

pwb_vic_h <- round(prop.table(table(factor(d$pwb_vic_h, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100,2)

pwb_vic_h <- as.data.frame(pwb_vic_h)
pwb_vic_h$group <- substring(row.names(pwb_vic_h), 1)
pwb_vic_h$group <- revalue(as.character(pwb_vic_h$group), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

pwb_vic_h$plot <- factor(pwb_vic_h$group, pwb_vic_h$group)
pwb_vic_h.plot <- ggplot(pwb_vic_h, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

      
pwb_vic_h.plot + geom_histogram(aes(x = plot), data = pwb_vic_h, stat = "identity") 

pwb_vic_h.tb <- as.factor(d$pwb_vic_h)
pwb_vic_h.tb <- summary(pwb_vic_h.tb)
pwb_vic_h.tb <- as.data.frame(pwb_vic_h.tb)
pwb_vic_h.tb$Var1 <- substring(row.names(pwb_vic_h.tb), 1)
                           
pwb_vic_h.tb$group <- revalue(as.character(pwb_vic_h.tb$Var1), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

plot.pwb_vic_h <- merge(pwb_vic_h, pwb_vic_h.tb, by = "group")
plot.pwb_vic_h <- plot.pwb_vic_h[-c(2, 4, 6)]
plot.pwb_vic_h <- setcolorder(plot.pwb_vic_h, c("group", "pwb_vic_h.tb", "Freq"))
plot.pwb_vic_h$order <- c(1, 10, 2, 3, 4, 5, 6, 7, 8, 9)
plot.pwb_vic_h <- plot.pwb_vic_h %>% arrange(order)
plot.pwb_vic_h <- plot.pwb_vic_h[-c(4)]
colnames(plot.pwb_vic_h) <- c("Response", "N", "Percentage")

kable(plot.pwb_vic_h) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

i. With your spirituality or religion?

#pwb_vic_i

pwb_vic_i <- round(prop.table(table(factor(d$pwb_vic_i, levels= c(1:10))))*100,2)

pwb_vic_i <- as.data.frame(pwb_vic_i)
pwb_vic_i$group <- substring(row.names(pwb_vic_i), 1)
pwb_vic_i$group <- revalue(as.character(pwb_vic_i$group), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

pwb_vic_i$plot <- factor(pwb_vic_i$group, pwb_vic_i$group)
pwb_vic_i.plot <- ggplot(pwb_vic_i, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

      
pwb_vic_i.plot + geom_histogram(aes(x = plot), data = pwb_vic_i, stat = "identity") 



pwb_vic_i.tb <- as.factor(d$pwb_vic_i)
pwb_vic_i.tb <- summary(pwb_vic_i.tb)
pwb_vic_i.tb <- as.data.frame(pwb_vic_i.tb)
pwb_vic_i.tb$Var1 <- substring(row.names(pwb_vic_i.tb), 1)

nval.df <- c("0") #insert missing values 
nval.df <- as.data.frame(nval.df)
nval.df$pwb_vic_i.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("9")
nval.df <- nval.df[-c(1)]

pwb_vic_i.tb <- rbind(pwb_vic_i.tb, nval.df)

pwb_vic_i.tb$group <- revalue(as.character(pwb_vic_i.tb$Var1), c("1" = "1- Completely satisfied", "10" = "10-Completely dissatisfied"))

plot.pwb_vic_i <- merge(pwb_vic_i, pwb_vic_i.tb, by = "group")
plot.pwb_vic_i <- plot.pwb_vic_i[-c(4, 6)]
plot.pwb_vic_i <- setcolorder(plot.pwb_vic_i, c("group", "pwb_vic_i.tb", "Freq", "Var1.x"))
plot.pwb_vic_i <- plot.pwb_vic_i %>% arrange(Var1.x)
plot.pwb_vic_i <- plot.pwb_vic_i[-c(4)]
colnames(plot.pwb_vic_i) <- c("Response", "N", "Percentage")

kable(plot.pwb_vic_i) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Q47: In general, I consider myself:

#gwb_a

gwb_a <- round(prop.table(table(factor(d$gwb_a, levels=c(1:7))))*100,2)
gwb_a <- as.data.frame(gwb_a)
gwb_a$group <- substring(row.names(gwb_a), 1)
gwb_a$group <- revalue(as.character(gwb_a$group), c("1" = "1- Not a very happy person", "7" = "7- A very happy person"))
gwb_a$plot <- factor(gwb_a$group, gwb_a$group)

p <- ggplot(gwb_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) #order responses as in gwb_a

p + geom_histogram(aes(x = plot), data = gwb_a, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")

gwb_a.tb <- as.factor(d$gwb_a)
gwb_a.tb <- summary(gwb_a.tb)
gwb_a.tb <- as.data.frame(gwb_a.tb)
gwb_a.tb$Var1 <- substring(row.names(gwb_a.tb), 1)

# nval.df <- c("0") #insert missing values 
# nval.df <- as.data.frame(nval.df)
# nval.df$gwb_a.tb <- as.factor(nval.df$nval.df)
# nval.df$Var1 <- c("1")
# nval.df <- nval.df[-c(1)]
# 
# gwb_a.tb <- rbind(gwb_a.tb, nval.df)

gwb_a.tb$group <- revalue(as.character(gwb_a.tb$Var1), c("1" = "1- Not a very happy person", "7" = "7- A very happy person"))

plot.gwb_a <- merge(gwb_a, gwb_a.tb, by = "group")
plot.gwb_a <- plot.gwb_a[-c(2, 4, 6)]
plot.gwb_a <- setcolorder(plot.gwb_a, c("group", "gwb_a.tb", "Freq"))
colnames(plot.gwb_a) <- c("Response", "N", "Percentage")

kable(plot.gwb_a) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Q48: Compared with most of my peers, I consider myself:

#gwb_b

gwb_b <- round(prop.table(table(factor(d$gwb_b)))*100,2)
gwb_b <- as.data.frame(gwb_b)
gwb_b$group <- substring(row.names(gwb_b), 1)
gwb_b$group <- revalue(as.character(gwb_b$group), c("1" = "1- Less happy", "7" = "7- More happy"))
gwb_b$plot <- factor(gwb_b$group, gwb_b$group)

p <- ggplot(gwb_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = gwb_b, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") 

gwb_b.tb <- as.factor(d$gwb_b)
gwb_b.tb <- summary(gwb_b.tb)
gwb_b.tb <- as.data.frame(gwb_b.tb)
gwb_b.tb$Var1 <- substring(row.names(gwb_b.tb), 1)
gwb_b.tb$group <- revalue(as.character(gwb_b.tb$Var1), c("1" = "1- Less happy", "7" = "7- More happy"))

plot.gwb_b <- merge(gwb_b, gwb_b.tb, by = "group")
plot.gwb_b <- plot.gwb_b[-c(2, 4, 6)]
plot.gwb_b <- setcolorder(plot.gwb_b, c("group", "gwb_b.tb", "Freq"))
colnames(plot.gwb_b) <- c("Response", "N", "Percentage")

kable(plot.gwb_b) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Q49: Some people are generally very happy. They enjoy life regardless of what is going on, getting the most out of everything. To what extent does this characterization describe you?


#gwb_c

gwb_c <- round(prop.table(table(factor(d$gwb_c)))*100,2)
gwb_c <- as.data.frame(gwb_c)
gwb_c$group <- substring(row.names(gwb_c), 1)
gwb_c$group <- revalue(as.character(gwb_c$group), c("1" = "1- Not at all", "7" = "7- A great deal"))
gwb_c$plot <- factor(gwb_c$group, gwb_c$group)

p <- ggplot(gwb_c, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = gwb_c, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") 

gwb_c.tb <- as.factor(d$gwb_c)
gwb_c.tb <- summary(gwb_c.tb)
gwb_c.tb <- as.data.frame(gwb_c.tb)
gwb_c.tb$Var1 <- substring(row.names(gwb_c.tb), 1)
gwb_c.tb$group <- revalue(as.character(gwb_c.tb$Var1), c("1" = "1- Not at all", "7" = "7- A great deal"))

plot.gwb_c <- merge(gwb_c, gwb_c.tb, by = "group")
plot.gwb_c <- plot.gwb_c[-c(2, 4, 6)]
plot.gwb_c <- setcolorder(plot.gwb_c, c("group", "gwb_c.tb", "Freq"))
colnames(plot.gwb_c) <- c("Response", "N", "Percentage")

kable(plot.gwb_c) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Q50: Some people are generally not very happy. Although they are not depressed, they never seem as happy as they might be. To what extent does this characterization describe you?


#gwb_d

gwb_d <- round(prop.table(table(factor(d$gwb_d, levels = 1:7)))*100,2)
gwb_d <- as.data.frame(gwb_d)
gwb_d$group <- substring(row.names(gwb_d), 1)
gwb_d$group <- revalue(as.character(gwb_d$group), c("1" = "1- Not at all", "7" = "7- A great deal"))
gwb_d$plot <- factor(gwb_d$group, gwb_d$group)

p <- ggplot(gwb_d, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = gwb_d, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")

gwb_d.tb <- as.factor(d$gwb_d)
gwb_d.tb <- summary(gwb_d.tb)
gwb_d.tb <- as.data.frame(gwb_d.tb)
gwb_d.tb$Var1 <- substring(row.names(gwb_d.tb), 1)
gwb_d.tb$group <- revalue(as.character(gwb_d.tb$Var1), c("1" = "1- Not at all", "7" = "7- A great deal"))

plot.gwb_d <- merge(gwb_d, gwb_d.tb, by = "group")
plot.gwb_d <- plot.gwb_d[-c(2, 4, 6)]
plot.gwb_d <- setcolorder(plot.gwb_d, c("group", "gwb_d.tb", "Freq"))
colnames(plot.gwb_d) <- c("Response", "N", "Percentage")

kable(plot.gwb_d) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Q51: The next questions are about how you feel about different aspects of your life. For each one, tell us how often you feel that way.

a. How often do you feel that you lack companionship?

#loneliness_a

loneliness_a <- round(prop.table(table(factor(d$loneliness_a, levels = 1:3)))*100,2)
loneliness_a <- as.data.frame(loneliness_a)
loneliness_a$group <- substring(row.names(loneliness_a), 1)
loneliness_a$group <- revalue(as.character(loneliness_a$group), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))
loneliness_a$plot <- factor(loneliness_a$group, loneliness_a$group)

p <- ggplot(loneliness_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = loneliness_a, stat = "identity") +  
  scale_fill_manual(values=rev(INTERACTshorterfade)) +
  guides(fill=FALSE) +
      ylab("Percent of total") +
      xlab("")

loneliness_a.tb <- as.factor(d$loneliness_a)
loneliness_a.tb <- summary(loneliness_a.tb)
loneliness_a.tb <- as.data.frame(loneliness_a.tb)
loneliness_a.tb$Var1 <- substring(row.names(loneliness_a.tb), 1)
loneliness_a.tb$group <- revalue(as.character(loneliness_a.tb$Var1), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))

plot.loneliness_a <- merge(loneliness_a, loneliness_a.tb, by = "group")
plot.loneliness_a <- plot.loneliness_a[-c(2, 4, 6)]
plot.loneliness_a <- setcolorder(plot.loneliness_a, c("group", "loneliness_a.tb", "Freq"))
plot.loneliness_a$order <- c(1, 3, 2)
plot.loneliness_a <- plot.loneliness_a %>% arrange(order)
plot.loneliness_a <- plot.loneliness_a[-c(4)]
colnames(plot.loneliness_a) <- c("Response", "N", "Percentage")

kable(plot.loneliness_a) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

b. How often do you feel left out?


#loneliness_b
loneliness_b <- round(prop.table(table(factor(d$loneliness_b, levels = 1:3)))*100,2)
loneliness_b <- as.data.frame(loneliness_b)
loneliness_b$group <- substring(row.names(loneliness_b), 1)
loneliness_b$group <- revalue(as.character(loneliness_b$group), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))
loneliness_b$plot <- factor(loneliness_b$group, loneliness_b$group)

p <- ggplot(loneliness_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = loneliness_b, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTshorterfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")

loneliness_b.tb <- as.factor(d$loneliness_b)
loneliness_b.tb <- summary(loneliness_b.tb)
loneliness_b.tb <- as.data.frame(loneliness_b.tb)
loneliness_b.tb$Var1 <- substring(row.names(loneliness_b.tb), 1)
loneliness_b.tb$group <- revalue(as.character(loneliness_b.tb$Var1), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))

plot.loneliness_b <- merge(loneliness_b, loneliness_b.tb, by = "group")
plot.loneliness_b <- plot.loneliness_b[-c(2, 4, 6)]
plot.loneliness_b <- setcolorder(plot.loneliness_b, c("group", "loneliness_b.tb", "Freq"))
plot.loneliness_b$order <- c(1, 3, 2)
plot.loneliness_b <- plot.loneliness_b %>% arrange(order)
plot.loneliness_b <- plot.loneliness_b[-c(4)]
colnames(plot.loneliness_b) <- c("Response", "N", "Percentage")

kable(plot.loneliness_b) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

c. How often do you feel isolated from others?

#loneliness_c
loneliness_c <- round(prop.table(table(factor(d$loneliness_c, levels = 1:3)))*100,2)
loneliness_c <- as.data.frame(loneliness_c)
loneliness_c$group <- substring(row.names(loneliness_c), 1)
loneliness_c$group <- revalue(as.character(loneliness_c$group), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))
loneliness_c$plot <- factor(loneliness_c$group, loneliness_c$group)

p <- ggplot(loneliness_c, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = loneliness_c, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTshorterfade)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")

loneliness_c.tb <- as.factor(d$loneliness_c)
loneliness_c.tb <- summary(loneliness_c.tb)
loneliness_c.tb <- as.data.frame(loneliness_c.tb)
loneliness_c.tb$Var1 <- substring(row.names(loneliness_c.tb), 1)
loneliness_c.tb$group <- revalue(as.character(loneliness_c.tb$Var1), c("1" = "Hardly ever", "2" = "Some of the time", "3" = "Often"))

plot.loneliness_c <- merge(loneliness_c, loneliness_c.tb, by = "group")
plot.loneliness_c <- plot.loneliness_c[-c(2, 4, 6)]
plot.loneliness_c <- setcolorder(plot.loneliness_c, c("group", "loneliness_c.tb", "Freq"))
plot.loneliness_c$order <- c(1, 3, 2)
plot.loneliness_c <- plot.loneliness_c %>% arrange(order)
plot.loneliness_c <- plot.loneliness_c[-c(4)]
colnames(plot.loneliness_c) <- c("Response", "N", "Percentage")

kable(plot.loneliness_c) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Section 5: Social Participation

Q52: How would you describe your sense of belonging to your local community? Would you say it is:


#belonging

## Plot
### Data preparation
belonging <- round(prop.table(table(factor(d$belonging)))*100,2)
belonging <- as.data.frame(belonging)
belonging$group <- substring(row.names(belonging), 1)
belonging$group <- revalue(as.character(belonging$group), c("1" = "Very strong", "2" = "Somewhat strong", "3" = "Somewhat weak", "4" = "Very weak", "5" ="I don't know"))
belonging$group <- factor(belonging$group, belonging$group)

p <- ggplot(belonging, aes(x=group, y=Freq, fill=group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = group), data = belonging, stat = "identity") +
    scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("Sense of belonging")

## Table
kable(data.frame(Response = c("Very strong", "Somewhat strong", "Somewhat weak", "Very weak", "I don't know"),
           Frequence = as.numeric(table(d$belonging)), Percentage = round(as.numeric(prop.table(table(d$belonging)))*100,2))) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Q53: How often do you:

a. Say hello to a neighbour?

#spat_a

#per week
ggplot(d, aes(x = d$spat_a/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_a/52.1429)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   0.0000   0.0000   0.9973   5.9906   4.9863 698.0816

b. Stop and have a chat with a neighbour?

#spat_b

#per week
ggplot(d, aes(x = d$spat_b/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_b/52.1429)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   0.0000   0.0000   0.2301   2.8338   0.9973 349.0408

c. Visit a neighbour, or receive a visit from a neighbour?

#spat_c

#per week
ggplot(d, aes(x = d$spat_c/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_c/52.1429)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.3068  0.2301  6.9808       1

d. Go somewhere (e.g., to a shop; to a restaurant), together with a neighbour?

#spat_d

#per week
ggplot(d, aes(x = d$spat_d/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_d/52.1429)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##  0.00000  0.00000  0.00000  0.17822  0.01918 13.96163        1

e. Ask help/advice from or do you help/give advice to a neighbour yourself?

#spat_e

#per week
ggplot(d, aes(x = d$spat_e/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")

summary(d$spat_e/52.1429)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.2388  0.2301  5.9836       1

Q54: Thinking about your neighbourhood, how would you rate the following statements?

a. This is a close-knit neighbourhood

# plot spat2_a

spat2_a <- round(prop.table(table(factor(d$spat2_a)))*100,2)
spat2_a <- as.data.frame(spat2_a)
spat2_a$group <- substring(row.names(spat2_a), 1)
spat2_a$group <- revalue(as.character(spat2_a$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_a$plot <- factor(spat2_a$group, spat2_a$group)

p <- ggplot(spat2_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_a, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("")+
      theme(plot.title = element_text(size=16))

spat2_a.tb <- as.factor(d$spat2_a)
spat2_a.tb <- summary(spat2_a.tb)
spat2_a.tb <- as.data.frame(spat2_a.tb)
spat2_a.tb$Var1 <- substring(row.names(spat2_a.tb), 1)
spat2_a.tb$group <- revalue(as.character(spat2_a.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_a.tb <- merge(spat2_a, spat2_a.tb, by = "group")
plot.spat2_a.tb <- plot.spat2_a.tb[-c(2, 4, 6)]
plot.spat2_a.tb <- setcolorder(plot.spat2_a.tb, c("group", "spat2_a.tb", "Freq"))

plot.spat2_a.tb$order <- c(3,4,2,5,1)
plot.spat2_a.tb <- plot.spat2_a.tb %>% arrange(order)
plot.spat2_a.tb <- plot.spat2_a.tb[-c(4)]
colnames(plot.spat2_a.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_a.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Strongly disagree 10 5.99
Somewhat disagree 35 20.96
Neither agree or disagree 70 41.92
Somewhat agree 43 25.75
Strongly agree 9 5.39

b. People generally do not get along

# plot spat2_b

spat2_b <- round(prop.table(table(factor(d$spat2_b)))*100,2)
spat2_b <- as.data.frame(spat2_b)
spat2_b$group <- substring(row.names(spat2_b), 1)
spat2_b$group <- revalue(as.character(spat2_b$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_b$plot <- factor(spat2_b$group, spat2_b$group)

p <- ggplot(spat2_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_b, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

spat2_b.tb <- as.factor(d$spat2_b)
spat2_b.tb <- summary(spat2_b.tb)
spat2_b.tb <- as.data.frame(spat2_b.tb)
spat2_b.tb$Var1 <- substring(row.names(spat2_b.tb), 1)
spat2_b.tb$group <- revalue(as.character(spat2_b.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_b.tb <- merge(spat2_b, spat2_b.tb, by = "group")
plot.spat2_b.tb <- plot.spat2_b.tb[-c(2, 4, 6)]
plot.spat2_b.tb <- setcolorder(plot.spat2_b.tb, c("group", "spat2_b.tb", "Freq"))

plot.spat2_b.tb$order <- c(3,4,2,5,1)
plot.spat2_b.tb <- plot.spat2_b.tb %>% arrange(order)
plot.spat2_b.tb <- plot.spat2_b.tb[-c(4)]
colnames(plot.spat2_b.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_b.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Strongly disagree 67 40.12
Somewhat disagree 63 37.72
Neither agree or disagree 26 15.57
Somewhat agree 9 5.39
Strongly agree 2 1.20

c. People are willing to help neighbours

# plot spat2_c
spat2_c <- round(prop.table(table(factor(d$spat2_c)))*100,2)
spat2_c <- as.data.frame(spat2_c)
spat2_c$group <- substring(row.names(spat2_c), 1)
spat2_c$group <- revalue(as.character(spat2_c$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_c$plot <- factor(spat2_c$group, spat2_c$group)

p <- ggplot(spat2_c, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_c, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

spat2_c.tb <- as.factor(d$spat2_c)
spat2_c.tb <- summary(spat2_c.tb)
spat2_c.tb <- as.data.frame(spat2_c.tb)
spat2_c.tb$Var1 <- substring(row.names(spat2_c.tb), 1)
spat2_c.tb$group <- revalue(as.character(spat2_c.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_c.tb <- merge(spat2_c, spat2_c.tb, by = "group")
plot.spat2_c.tb <- plot.spat2_c.tb[-c(2, 4, 6)]
plot.spat2_c.tb <- setcolorder(plot.spat2_c.tb, c("group", "spat2_c.tb", "Freq"))

plot.spat2_c.tb$order <- c(3,4,2,5,1)
plot.spat2_c.tb <- plot.spat2_c.tb %>% arrange(order)
plot.spat2_c.tb <- plot.spat2_c.tb[-c(4)]
colnames(plot.spat2_c.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_c.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Strongly disagree 2 1.20
Somewhat disagree 6 3.59
Neither agree or disagree 43 25.75
Somewhat agree 88 52.69
Strongly agree 28 16.77

d. People do not share same values

# plot spat2_d

spat2_d <- round(prop.table(table(factor(d$spat2_d)))*100,2)
spat2_d <- as.data.frame(spat2_d)
spat2_d$group <- substring(row.names(spat2_d), 1)
spat2_d$group <- revalue(as.character(spat2_d$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_d$plot <- factor(spat2_d$group, spat2_d$group)

p <- ggplot(spat2_d, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_d, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

spat2_d.tb <- as.factor(d$spat2_d)
spat2_d.tb <- summary(spat2_d.tb)
spat2_d.tb <- as.data.frame(spat2_d.tb)
spat2_d.tb$Var1 <- substring(row.names(spat2_d.tb), 1)
spat2_d.tb$group <- revalue(as.character(spat2_d.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_d.tb <- merge(spat2_d, spat2_d.tb, by = "group")
plot.spat2_d.tb <- plot.spat2_d.tb[-c(2, 4, 6)]
plot.spat2_d.tb <- setcolorder(plot.spat2_d.tb, c("group", "spat2_d.tb", "Freq"))

plot.spat2_d.tb$order <- c(3,4,2,5,1)
plot.spat2_d.tb <- plot.spat2_d.tb %>% arrange(order)
plot.spat2_d.tb <- plot.spat2_d.tb[-c(4)]
colnames(plot.spat2_d.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_d.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Strongly disagree 23 13.77
Somewhat disagree 67 40.12
Neither agree or disagree 60 35.93
Somewhat agree 13 7.78
Strongly agree 4 2.40

e. People can be trusted

# plot spat2_e

spat2_e <- round(prop.table(table(factor(d$spat2_e)))*100,2)
spat2_e <- as.data.frame(spat2_e)
spat2_e$group <- substring(row.names(spat2_e), 1)
spat2_e$group <- revalue(as.character(spat2_e$group), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))
spat2_e$plot <- factor(spat2_e$group, spat2_e$group)

p <- ggplot(spat2_e, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat2_e, stat = "identity") +
  scale_fill_manual(values=rev(INTERACTPalette3)) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

spat2_e.tb <- as.factor(d$spat2_e)
spat2_e.tb <- summary(spat2_e.tb)
spat2_e.tb <- as.data.frame(spat2_e.tb)
spat2_e.tb$Var1 <- substring(row.names(spat2_e.tb), 1)
spat2_e.tb$group <- revalue(as.character(spat2_e.tb$Var1), c("1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree"))

plot.spat2_e.tb <- merge(spat2_e, spat2_e.tb, by = "group")
plot.spat2_e.tb <- plot.spat2_e.tb[-c(2, 4, 6)]
plot.spat2_e.tb <- setcolorder(plot.spat2_e.tb, c("group", "spat2_e.tb", "Freq"))

plot.spat2_e.tb$order <- c(3,4,2,5,1)
plot.spat2_e.tb <- plot.spat2_e.tb %>% arrange(order)
plot.spat2_e.tb <- plot.spat2_e.tb[-c(4)]
colnames(plot.spat2_e.tb) <- c("Response", "N", "Percentage")

kable(plot.spat2_e.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Strongly disagree 3 1.80
Somewhat disagree 7 4.19
Neither agree or disagree 27 16.17
Somewhat agree 95 56.89
Strongly agree 35 20.96

Q55: If you lost a wallet or purse that contained two hundred dollars, how likely is it to be returned with the money in it, if it was found:

a. By someone who lives close by? Would it be:

#spat3_a

spat3_a <- round(prop.table(table(factor(d$spat3_a)))*100,2)
spat3_a <- as.data.frame(spat3_a)
spat3_a$group <- substring(row.names(spat3_a), 1)
spat3_a$group <- revalue(as.character(spat3_a$group), c("1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "4"="I don't know"))
spat3_a$plot <- factor(spat3_a$group, spat3_a$group)

p <- ggplot(spat3_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat3_a, stat = "identity") +
      scale_fill_manual(values = INTERACTPalette3) +
      guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

# make a clean summary table 
## make a dataframe on count 
spat3_a.tb <- as.factor(d$spat3_a)
spat3_a.tb <- summary(spat3_a.tb)
spat3_a.tb <- as.data.frame(spat3_a.tb)
spat3_a.tb$Var1 <- substring(row.names(spat3_a.tb), 1)
spat3_a.tb$group <- revalue(as.character(spat3_a.tb$Var1), c("1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "77"="I don't know"))


## merge with existing prop table data used for plot above 
plot.spat3_a.tb <- merge(spat3_a, spat3_a.tb, by = "group")
plot.spat3_a.tb <- plot.spat3_a.tb[-c(2, 4, 6)]
plot.spat3_a.tb <- setcolorder(plot.spat3_a.tb, c("group", "spat3_a.tb", "Freq"))

plot.spat3_a.tb$order <- c(3,2,1)
plot.spat3_a.tb <- plot.spat3_a.tb %>% arrange(order)
plot.spat3_a.tb <- plot.spat3_a.tb[-c(4)]
colnames(plot.spat3_a.tb) <- c("Response", "N", "Percentage")

kable(plot.spat3_a.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very likely 83 49.70
Somewhat likely 66 39.52
Not at all likely 6 3.59

b. By a complete stranger? Would it be:

#spat3_b
spat3_b <- round(prop.table(table(factor(d$spat3_b)))*100,2)
spat3_b <- as.data.frame(spat3_b)
spat3_b$group <- substring(row.names(spat3_b), 1)
spat3_b$group <- revalue(as.character(spat3_b$group), c("1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "4"="I don't know"))
spat3_b$plot <- factor(spat3_b$group, spat3_b$group)

p <- ggplot(spat3_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = spat3_b, stat = "identity") +
      scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE)+
      ylab("Percent of total") +
      xlab("") +
      theme(plot.title = element_text(size=16))

# make a clean summary table 
## make a dataframe on count 
spat3_b.tb <- as.factor(d$spat3_b)
spat3_b.tb <- summary(spat3_b.tb)
spat3_b.tb <- as.data.frame(spat3_b.tb)
spat3_b.tb$Var1 <- substring(row.names(spat3_b.tb), 1)
spat3_b.tb$group <- revalue(as.character(spat3_b.tb$Var1), c("1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "77"="I don't know"))

## merge with existing prop table data used for plot above 
plot.spat3_b.tb <- merge(spat3_b, spat3_b.tb, by = "group")
plot.spat3_b.tb <- plot.spat3_b.tb[-c(2, 4, 6)]
plot.spat3_b.tb <- setcolorder(plot.spat3_b.tb, c("group", "spat3_b.tb", "Freq"))

plot.spat3_b.tb$order <- c(3,2,1)
plot.spat3_b.tb <- plot.spat3_b.tb %>% arrange(order)
plot.spat3_b.tb <- plot.spat3_b.tb[-c(4)]
colnames(plot.spat3_b.tb) <- c("Response", "N", "Percentage")

kable(plot.spat3_b.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very likely 8 4.79
Somewhat likely 93 55.69
Not at all likely 46 27.54

Q56: Here are some questions about your satisfaction with the neighbourhood in which you live. Please indicate your satisfaction with each item.How satisfied are you with…

a. your neighbourhood as a good place to live?

#neighb_a

neighb_a <- round(prop.table(table(factor(d$neighb_a)))*100,2)
neighb_a <- as.data.frame(neighb_a)
neighb_a$group <- substring(row.names(neighb_a), 1)
neighb_a$group <- revalue(as.character(neighb_a$group), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

neighb_a$plot <- factor(neighb_a$group, neighb_a$group)
neighb_a.plot <- ggplot(neighb_a, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPalette3) +
      ylab("Percent of total") +
      xlab("")
      
neighb_a.plot + geom_histogram(aes(x = plot), data = neighb_a, stat = "identity") 

# make a clean summary table 
## make a dataframe on count 
neighb_a.tb <- as.factor(d$neighb_a)
neighb_a.tb <- summary(neighb_a.tb)
neighb_a.tb <- as.data.frame(neighb_a.tb)
neighb_a.tb$Var1 <- substring(row.names(neighb_a.tb), 1)
neighb_a.tb$group <- revalue(as.character(neighb_a.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))


## merge with existing prop table data used for plot above 
plot.neighb_a.tb <- merge(neighb_a, neighb_a.tb, by = "group")
plot.neighb_a.tb <- plot.neighb_a.tb[-c(2, 4, 6)]
plot.neighb_a.tb <- setcolorder(plot.neighb_a.tb, c("group", "neighb_a.tb", "Freq"))

plot.neighb_a.tb$order <- c(4,3,2,5,1)
plot.neighb_a.tb <- plot.neighb_a.tb %>% arrange(order)
plot.neighb_a.tb <- plot.neighb_a.tb[-c(4)]
colnames(plot.neighb_a.tb) <- c("Response", "N", "Percentage")

kable(plot.neighb_a.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Strongly satisfied 129 45.91
Satisfied 123 43.77
Neither satisfied nor dissatisfied 19 6.76
Dissatisfied 9 3.20
Strongly dissatisfied 1 0.36

b. the number of people you know in your neighbourhood?

#neighb_b

neighb_b <- round(prop.table(table(factor(d$neighb_b)))*100,2)
neighb_b <- as.data.frame(neighb_b)
neighb_b$group <- substring(row.names(neighb_b), 1)
neighb_b$group <- revalue(as.character(neighb_b$group), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

neighb_b$plot <- factor(neighb_b$group, neighb_b$group)
neighb_b.plot <- ggplot(neighb_b, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPalette3) +
      ylab("Percent of total") +
      xlab("")
      
neighb_b.plot + geom_histogram(aes(x = plot), data = neighb_b, stat = "identity") 

# make a clean summary table 
## make a dataframe on count 
neighb_b.tb <- as.factor(d$neighb_b)
neighb_b.tb <- summary(neighb_b.tb)
neighb_b.tb <- as.data.frame(neighb_b.tb)
neighb_b.tb$Var1 <- substring(row.names(neighb_b.tb), 1)
neighb_b.tb$group <- revalue(as.character(neighb_b.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

## merge with existing prop table data used for plot above 
plot.neighb_b.tb <- merge(neighb_b, neighb_b.tb, by = "group")
plot.neighb_b.tb <- plot.neighb_b.tb[-c(2, 4, 6)]
plot.neighb_b.tb <- setcolorder(plot.neighb_b.tb, c("group", "neighb_b.tb", "Freq"))

plot.neighb_b.tb$order <- c(4,3,2,5,1)
plot.neighb_b.tb <- plot.neighb_b.tb %>% arrange(order)
plot.neighb_b.tb <- plot.neighb_b.tb[-c(4)]
colnames(plot.neighb_b.tb) <- c("Response", "N", "Percentage")

kable(plot.neighb_b.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Strongly satisfied 40 14.23
Satisfied 112 39.86
Neither satisfied nor dissatisfied 84 29.89
Dissatisfied 41 14.59
Strongly dissatisfied 4 1.42

c. the ethnic diversity of your neighbourhood?

#neighb_c

neighb_c <- round(prop.table(table(factor(d$neighb_c)))*100,2)
neighb_c <- as.data.frame(neighb_c)
neighb_c$group <- substring(row.names(neighb_c), 1)
neighb_c$group <- revalue(as.character(neighb_c$group), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

neighb_c$plot <- factor(neighb_c$group, neighb_c$group)

neighb_c.plot <- ggplot(neighb_c, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPalette3) +
      ylab("Percent of total") +
      xlab("")
      
neighb_c.plot + geom_histogram(aes(x = plot), data = neighb_c, stat = "identity") 

# make a clean summary table 
## make a dataframe on count 
neighb_c.tb <- as.factor(d$neighb_c)
neighb_c.tb <- summary(neighb_c.tb)
neighb_c.tb <- as.data.frame(neighb_c.tb)
neighb_c.tb$Var1 <- substring(row.names(neighb_c.tb), 1)
neighb_c.tb$group <- revalue(as.character(neighb_c.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

## merge with existing prop table data used for plot above 
plot.neighb_c.tb <- merge(neighb_c, neighb_c.tb, by = "group")
plot.neighb_c.tb <- plot.neighb_c.tb[-c(2, 4, 6)]
plot.neighb_c.tb <- setcolorder(plot.neighb_c.tb, c("group", "neighb_c.tb", "Freq"))

plot.neighb_c.tb$order <- c(4,3,2,5,1)
plot.neighb_c.tb <- plot.neighb_c.tb %>% arrange(order)
plot.neighb_c.tb <- plot.neighb_c.tb[-c(4)]
colnames(plot.neighb_c.tb) <- c("Response", "N", "Percentage")

kable(plot.neighb_c.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Strongly satisfied 27 9.61
Satisfied 79 28.11
Neither satisfied nor dissatisfied 127 45.20
Dissatisfied 40 14.23
Strongly dissatisfied 8 2.85

d. your neighbourhood as a good place to raise children

#neighb_d

neighb_d <- round(prop.table(table(factor(d$neighb_d)))*100,2)
neighb_d <- as.data.frame(neighb_d)
neighb_d$group <- substring(row.names(neighb_d), 1)
neighb_d$group <- revalue(as.character(neighb_d$group), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))

neighb_d$plot <- factor(neighb_d$group, neighb_d$group)

neighb_d.plot <- ggplot(neighb_d, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPalette3) +
      ylab("Percent of total") +
      xlab("")
      
neighb_d.plot + geom_histogram(aes(x = plot), data = neighb_d, stat = "identity") 

# make a clean summary table 
## make a dataframe on count 
neighb_d.tb <- as.factor(d$neighb_d)
neighb_d.tb <- summary(neighb_d.tb)
neighb_d.tb <- as.data.frame(neighb_d.tb)
neighb_d.tb$Var1 <- substring(row.names(neighb_d.tb), 1)
neighb_d.tb$group <- revalue(as.character(neighb_d.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied"))


## merge with existing prop table data used for plot above 
plot.neighb_d.tb <- merge(neighb_d, neighb_d.tb, by = "group")
plot.neighb_d.tb <- plot.neighb_d.tb[-c(2, 4, 6)]
plot.neighb_d.tb <- setcolorder(plot.neighb_d.tb, c("group", "neighb_d.tb", "Freq"))

plot.neighb_d.tb$order <- c(4,3,2,5,1)
plot.neighb_d.tb <- plot.neighb_d.tb %>% arrange(order)
plot.neighb_d.tb <- plot.neighb_d.tb[-c(4)]
colnames(plot.neighb_d.tb) <- c("Response", "N", "Percentage")

kable(plot.neighb_d.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Strongly satisfied 80 28.47
Satisfied 115 40.93
Neither satisfied nor dissatisfied 74 26.33
Dissatisfied 10 3.56
Strongly dissatisfied 2 0.71

Section 7: Neighbourhood Selection

Q57: Before moving into your current dwelling, when you were looking for a neighbourhood to live in, to what extent were the following characteristics important? Please report your perspectives, even if the neighbourhood where you currently live does not have these characteristics

a. Good access to public transportation

#neigh_pref_a

neigh_pref_a <- round(prop.table(table(factor(d$neigh_pref_a)))*100,2)
neigh_pref_a <- as.data.frame(neigh_pref_a)
neigh_pref_a$group <- substring(row.names(neigh_pref_a), 1)
neigh_pref_a$group <- revalue(as.character(neigh_pref_a$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_a$plot <- factor(neigh_pref_a$group, neigh_pref_a$group)

p <- ggplot(neigh_pref_a, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_a, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total")

# make a clean summary table 
## make a dataframe on count 
neigh_pref_a.tb <- as.factor(d$neigh_pref_a)
neigh_pref_a.tb <- summary(neigh_pref_a.tb)
neigh_pref_a.tb <- as.data.frame(neigh_pref_a.tb)
neigh_pref_a.tb$Var1 <- substring(row.names(neigh_pref_a.tb), 1)
neigh_pref_a.tb$group <- revalue(as.character(neigh_pref_a.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_a.tb <- merge(neigh_pref_a, neigh_pref_a.tb, by = "group")
plot.neigh_pref_a.tb <- plot.neigh_pref_a.tb[-c(2, 4, 6)]
plot.neigh_pref_a.tb <- setcolorder(plot.neigh_pref_a.tb, c("group", "neigh_pref_a.tb", "Freq"))

plot.neigh_pref_a.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_a.tb <- plot.neigh_pref_a.tb %>% arrange(order)
plot.neigh_pref_a.tb <- plot.neigh_pref_a.tb[-c(4)]
colnames(plot.neigh_pref_a.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_a.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very important 101 35.94
Somewhat important 101 35.94
Not very important 52 18.51
Not important at all 26 9.25
I don’t know 1 0.36

b. Sufficient parks and green spaces

#neigh_pref_b

neigh_pref_b <- round(prop.table(table(factor(d$neigh_pref_b)))*100,2)
neigh_pref_b <- as.data.frame(neigh_pref_b)
neigh_pref_b$group <- substring(row.names(neigh_pref_b), 1)
neigh_pref_b$group <- revalue(as.character(neigh_pref_b$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_b$plot <- factor(neigh_pref_b$group, neigh_pref_b$group)

p <- ggplot(neigh_pref_b, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_b, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade)+
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("")

# make a clean summary table 
## make a dataframe on count 
neigh_pref_b.tb <- as.factor(d$neigh_pref_b)
neigh_pref_b.tb <- summary(neigh_pref_b.tb)
neigh_pref_b.tb <- as.data.frame(neigh_pref_b.tb)
neigh_pref_b.tb$Var1 <- substring(row.names(neigh_pref_b.tb), 1)
neigh_pref_b.tb$group <- revalue(as.character(neigh_pref_b.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))

## merge with existing prop table data used for plot above 
plot.neigh_pref_b.tb <- merge(neigh_pref_b, neigh_pref_b.tb, by = "group")
plot.neigh_pref_b.tb <- plot.neigh_pref_b.tb[-c(2, 4, 6)]
plot.neigh_pref_b.tb <- setcolorder(plot.neigh_pref_b.tb, c("group", "neigh_pref_b.tb", "Freq"))

plot.neigh_pref_b.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_b.tb <- plot.neigh_pref_b.tb %>% arrange(order)
plot.neigh_pref_b.tb <- plot.neigh_pref_b.tb[-c(4)]
colnames(plot.neigh_pref_b.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_b.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very important 173 61.57
Somewhat important 92 32.74
Not very important 11 3.91
Not important at all 4 1.42
I don’t know 1 0.36

c. Sufficient shops and services

#neigh_pref_c

neigh_pref_c <- round(prop.table(table(factor(d$neigh_pref_c)))*100,2)
neigh_pref_c <- as.data.frame(neigh_pref_c)
neigh_pref_c$group <- substring(row.names(neigh_pref_c), 1)
neigh_pref_c$group <- revalue(as.character(neigh_pref_c$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_c$plot <- factor(neigh_pref_c$group, neigh_pref_c$group)

p <- ggplot(neigh_pref_c, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_c, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_c.tb <- as.factor(d$neigh_pref_c)
neigh_pref_c.tb <- summary(neigh_pref_c.tb)
neigh_pref_c.tb <- as.data.frame(neigh_pref_c.tb)
neigh_pref_c.tb$Var1 <- substring(row.names(neigh_pref_c.tb), 1)
neigh_pref_c.tb$group <- revalue(as.character(neigh_pref_c.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_c.tb <- merge(neigh_pref_c, neigh_pref_c.tb, by = "group")
plot.neigh_pref_c.tb <- plot.neigh_pref_c.tb[-c(2, 4, 6)]
plot.neigh_pref_c.tb <- setcolorder(plot.neigh_pref_c.tb, c("group", "neigh_pref_c.tb", "Freq"))

plot.neigh_pref_c.tb$order <- c(4,3,2,1)
plot.neigh_pref_c.tb <- plot.neigh_pref_c.tb %>% arrange(order)
plot.neigh_pref_c.tb <- plot.neigh_pref_c.tb[-c(4)]
colnames(plot.neigh_pref_c.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_c.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very important 108 38.43
Somewhat important 132 46.98
Not very important 32 11.39
Not important at all 9 3.20

d. Proximity to doctors, a pharmacy or other health services

#neigh_pref_d

neigh_pref_d <- round(prop.table(table(factor(d$neigh_pref_d)))*100,2)
neigh_pref_d <- as.data.frame(neigh_pref_d)
neigh_pref_d$group <- substring(row.names(neigh_pref_d), 1)
neigh_pref_d$group <- revalue(as.character(neigh_pref_d$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_d$plot <- factor(neigh_pref_d$group, neigh_pref_d$group)

p <- ggplot(neigh_pref_d, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_d, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_d.tb <- as.factor(d$neigh_pref_d)
neigh_pref_d.tb <- summary(neigh_pref_d.tb)
neigh_pref_d.tb <- as.data.frame(neigh_pref_d.tb)
neigh_pref_d.tb$Var1 <- substring(row.names(neigh_pref_d.tb), 1)
neigh_pref_d.tb$group <- revalue(as.character(neigh_pref_d.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_d.tb <- merge(neigh_pref_d, neigh_pref_d.tb, by = "group")
plot.neigh_pref_d.tb <- plot.neigh_pref_d.tb[-c(2, 4, 6)]
plot.neigh_pref_d.tb <- setcolorder(plot.neigh_pref_d.tb, c("group", "neigh_pref_d.tb", "Freq"))

plot.neigh_pref_d.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_d.tb <- plot.neigh_pref_d.tb %>% arrange(order)
plot.neigh_pref_d.tb <- plot.neigh_pref_d.tb[-c(4)]
colnames(plot.neigh_pref_d.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_d.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very important 26 9.25
Somewhat important 109 38.79
Not very important 106 37.72
Not important at all 39 13.88
I don’t know 1 0.36

e. A good knowledge of the neighbourhood

#neigh_pref_e

neigh_pref_e <- round(prop.table(table(factor(d$neigh_pref_e)))*100,2)
neigh_pref_e <- as.data.frame(neigh_pref_e)
neigh_pref_e$group <- substring(row.names(neigh_pref_e), 1)
neigh_pref_e$group <- revalue(as.character(neigh_pref_e$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_e$plot <- factor(neigh_pref_e$group, neigh_pref_e$group)

p <- ggplot(neigh_pref_e, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_e, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("")

# make a clean summary table 
## make a dataframe on count 
neigh_pref_e.tb <- as.factor(d$neigh_pref_e)
neigh_pref_e.tb <- summary(neigh_pref_e.tb)
neigh_pref_e.tb <- as.data.frame(neigh_pref_e.tb)
neigh_pref_e.tb$Var1 <- substring(row.names(neigh_pref_e.tb), 1)
neigh_pref_e.tb$group <- revalue(as.character(neigh_pref_e.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_e.tb <- merge(neigh_pref_e, neigh_pref_e.tb, by = "group")
plot.neigh_pref_e.tb <- plot.neigh_pref_e.tb[-c(2, 4, 6)]
plot.neigh_pref_e.tb <- setcolorder(plot.neigh_pref_e.tb, c("group", "neigh_pref_e.tb", "Freq"))

plot.neigh_pref_e.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_e.tb <- plot.neigh_pref_e.tb %>% arrange(order)
plot.neigh_pref_e.tb <- plot.neigh_pref_e.tb[-c(4)]
colnames(plot.neigh_pref_e.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_e.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very important 56 19.93
Somewhat important 133 47.33
Not very important 69 24.56
Not important at all 21 7.47
I don’t know 2 0.71

f. Presence of relatives, friends or acquaintances

#neigh_pref_f

neigh_pref_f <- round(prop.table(table(factor(d$neigh_pref_f)))*100,2)
neigh_pref_f <- as.data.frame(neigh_pref_f)
neigh_pref_f$group <- substring(row.names(neigh_pref_f), 1)
neigh_pref_f$group <- revalue(as.character(neigh_pref_f$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_f$plot <- factor(neigh_pref_f$group, neigh_pref_f$group)

p <- ggplot(neigh_pref_f, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_f, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_f.tb <- as.factor(d$neigh_pref_f)
neigh_pref_f.tb <- summary(neigh_pref_f.tb)
neigh_pref_f.tb <- as.data.frame(neigh_pref_f.tb)
neigh_pref_f.tb$Var1 <- substring(row.names(neigh_pref_f.tb), 1)
neigh_pref_f.tb$group <- revalue(as.character(neigh_pref_f.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))


## merge with existing prop table data used for plot above 
plot.neigh_pref_f.tb <- merge(neigh_pref_f, neigh_pref_f.tb, by = "group")
plot.neigh_pref_f.tb <- plot.neigh_pref_f.tb[-c(2, 4, 6)]
plot.neigh_pref_f.tb <- setcolorder(plot.neigh_pref_f.tb, c("group", "neigh_pref_f.tb", "Freq"))

plot.neigh_pref_f.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_f.tb <- plot.neigh_pref_f.tb %>% arrange(order)
plot.neigh_pref_f.tb <- plot.neigh_pref_f.tb[-c(4)]
colnames(plot.neigh_pref_f.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_f.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very important 40 14.23
Somewhat important 97 34.52
Not very important 80 28.47
Not important at all 60 21.35
I don’t know 4 1.42

g. A neighbourhood where it is pleasant to walk

#neigh_pref_g

neigh_pref_g <- round(prop.table(table(factor(d$neigh_pref_g)))*100,2)
neigh_pref_g <- as.data.frame(neigh_pref_g)
neigh_pref_g$group <- substring(row.names(neigh_pref_g), 1)
neigh_pref_g$group <- revalue(as.character(neigh_pref_g$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_g$plot <- factor(neigh_pref_g$group, neigh_pref_g$group)

p <- ggplot(neigh_pref_g, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_g, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_g.tb <- as.factor(d$neigh_pref_g)
neigh_pref_g.tb <- summary(neigh_pref_g.tb)
neigh_pref_g.tb <- as.data.frame(neigh_pref_g.tb)
neigh_pref_g.tb$Var1 <- substring(row.names(neigh_pref_g.tb), 1)
neigh_pref_g.tb$group <- revalue(as.character(neigh_pref_g.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))

## merge with existing prop table data used for plot above 
plot.neigh_pref_g.tb <- merge(neigh_pref_g, neigh_pref_g.tb, by = "group")
plot.neigh_pref_g.tb <- plot.neigh_pref_g.tb[-c(2, 4, 6)]
plot.neigh_pref_g.tb <- setcolorder(plot.neigh_pref_g.tb, c("group", "neigh_pref_g.tb", "Freq"))

plot.neigh_pref_g.tb$order <- c(5,4,3,2, 1)
plot.neigh_pref_g.tb <- plot.neigh_pref_g.tb %>% arrange(order)
plot.neigh_pref_g.tb <- plot.neigh_pref_g.tb[-c(4)]
colnames(plot.neigh_pref_g.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_g.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very important 181 64.41
Somewhat important 85 30.25
Not very important 9 3.20
Not important at all 5 1.78
I don’t know 1 0.36

h. A neighbourhood where it is practical to move around by car (ease of parking, low traffic, good access by car)

#neigh_pref_h

neigh_pref_h <- round(prop.table(table(factor(d$neigh_pref_h)))*100,2)
neigh_pref_h <- as.data.frame(neigh_pref_h)
neigh_pref_h$group <- substring(row.names(neigh_pref_h), 1)
neigh_pref_h$group <- revalue(as.character(neigh_pref_h$group), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "I don't know"))
neigh_pref_h$plot <- factor(neigh_pref_h$group, neigh_pref_h$group)

p <- ggplot(neigh_pref_h, aes(x=group, y=Freq, fill=plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = plot), data = neigh_pref_h, stat = "identity") +
  scale_fill_manual(values=INTERACTshortfade) +
  guides(fill=FALSE)+
  ylab("Percent of total") +
  xlab("") 

# make a clean summary table 
## make a dataframe on count 
neigh_pref_h.tb <- as.factor(d$neigh_pref_h)
neigh_pref_h.tb <- summary(neigh_pref_h.tb)
neigh_pref_h.tb <- as.data.frame(neigh_pref_h.tb)
neigh_pref_h.tb$Var1 <- substring(row.names(neigh_pref_h.tb), 1)
neigh_pref_h.tb$group <- revalue(as.character(neigh_pref_h.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know"))

## merge with existing prop table data used for plot above 
plot.neigh_pref_h.tb <- merge(neigh_pref_h, neigh_pref_h.tb, by = "group")
plot.neigh_pref_h.tb <- plot.neigh_pref_h.tb[-c(2, 4, 6)]
plot.neigh_pref_h.tb <- setcolorder(plot.neigh_pref_h.tb, c("group", "neigh_pref_h.tb", "Freq"))

plot.neigh_pref_h.tb$order <- c(5,4,3,2,1)
plot.neigh_pref_h.tb <- plot.neigh_pref_h.tb %>% arrange(order)
plot.neigh_pref_h.tb <- plot.neigh_pref_h.tb[-c(4)]
colnames(plot.neigh_pref_h.tb) <- c("Response", "N", "Percentage")

kable(plot.neigh_pref_h.tb)%>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Very important 18 6.41
Somewhat important 76 27.05
Not very important 97 34.52
Not important at all 89 31.67
I don’t know 1 0.36

Section 9: Demographics

Q65: What is your current gender identity?

#gender 

gender <- round(prop.table(table(factor(d$gender_vic, levels = c("[1]", "[2]", "[3]", "[4]", "[5]", "[6]"))))*100,2)
gender <- as.data.frame(gender)
gender$response <- substring(row.names(gender), 1)
gender$response <- revalue(as.factor(gender$response), c("[1]"="Man","[2]"="Woman","[3]"="Trans man", "[4]"="Trans woman", "[5]"="Genderqueer/Gender non-conforming", "[6]"="Different identity"))

gender$response <- factor(gender$response, gender$response)

p <- ggplot(gender, aes(x = response, y = Freq, fill = response)) + theme(axis.text.x = element_text(size=12, angle=0, vjust = .6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = response), data = gender, stat = "identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent of total") +
      xlab("Gender") 

gender.tb <- as.factor(d$gender) 
gender.tb <- summary(gender.tb)
gender.tb <- as.data.frame(gender.tb)
gender.tb$Var1 <- substring(row.names(gender.tb), 1)


nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$gender.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("3", "4")
nval.df <- nval.df[-c(1)]

gender.tb <- rbind(gender.tb, nval.df)

gender.tb$response <- revalue(as.character(gender.tb$Var1), c("1"="Man","2"="Woman","3"="Trans man", "4"="Trans woman", "5"="Genderqueer/Gender non-conforming", "6"="Different identity"))

plot.gender <- merge(gender, gender.tb, by = "response")
plot.gender <- plot.gender[-c(5)]
plot.gender <- setcolorder(plot.gender, c("response", "gender.tb", "Freq", "Var1.x"))

plot.gender <- plot.gender %>% arrange(Var1.x)
plot.gender <- plot.gender[-c(4)]
colnames(plot.gender) <- c("Response", "N", "Percentage")


kable(plot.gender) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage

Q66: What sex were you assigned at birth?

# Sex

sex <- prop.table(table(factor(d$sex_vic, levels = 1:3)))*100
sex <- as.data.frame(sex)
sex$response <- substring(row.names(sex), 1)
sex$response <- revalue(as.factor(sex$response), c("1" = "Male", "2" = "Female", "3" = "Other"))

sex$response <- factor(sex$response, sex$response)

p <- ggplot(sex, aes(x = response, y = Freq, fill = response)) + theme(axis.text.x = element_text(size=12, angle=0, vjust = .6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

p + geom_histogram(aes(x = response), data = sex, stat = "identity") +
 scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent of total") +
      xlab("Sex") 

## Table- 
kable(data.frame(Response = c("Male","Female"),
           Frequence = as.numeric(table(d$sex)), 
           Percentage = round(as.numeric(prop.table(table(d$sex)))*100,2))) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Q67: What is your marital status? Are you…

#marital_status 

marital <- prop.table(table(factor(d$marital_status, levels = c("1", "2", "3", "4"))))*100
marital <- as.data.frame(marital)
marital$group <- substring(row.names(marital), 1)
marital$group <- revalue(as.character(marital$group), c("1" = "Single", "2" = "Married/commonlaw", "3" = "Separated/divorced", "4" = "Widowed"))

marital$plot <- factor(marital$group, marital$group)

marital.plot <- ggplot(marital, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteSet)  +
      ylab("Percent of total") +
      xlab("")
      
marital.plot + geom_histogram(aes(x = plot), data = marital, stat = "identity") 

## Table- 
kable(data.frame(Response = c("Single", "Married/commonlaw", "Separated/divorced", "Widowed"),
           Frequence = as.numeric(table(d$marital_status)), Percentage = round(as.numeric(prop.table(table(d$marital_status)))*100,2))) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response Frequence Percentage
Single 54 19.22
Married/commonlaw 202 71.89
Separated/divorced 23 8.19
Widowed 2 0.71

Q68: Do you have children?

#children

children <- prop.table(table(factor(d$children, levels = c("1", "2"))))*100
children <- as.data.frame(children)
children$group <- substring(row.names(children), 1)
children$group <- revalue(as.character(children$group), c("1" = "Yes",  "2" = "No"))

children$plot <- factor(children$group, children$group)

children.plot <- ggplot(children, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) #order responses as in t5

children.plot + geom_histogram(aes(x = plot), data = children, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

## Table- 
kable(data.frame(Response = c("Yes", "No"),
           Frequence = as.numeric(table(d$children)), Percentage = round(as.numeric(prop.table(table(d$children)))*100,2))) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response Frequence Percentage
Yes 151 53.74
No 130 46.26

Q69: How many children do you have?

#living_children

d$living_children[d$living_children==-7] <- NA

living_children <- round(prop.table(table(factor(d$living_children)))*100,2)
living_children <- as.data.frame(living_children)
living_children <- as.data.frame(living_children)
living_children$answer <- substring(row.names(living_children), 1)
living_children$answer <- revalue(as.character(living_children$answer))

living_children$plot <- factor(living_children$answer, living_children$answer)

living_children.plot <- ggplot(living_children, aes(x = answer, y = Freq, fill = plot, na.rm = TRUE)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

living_children.plot + geom_histogram(aes(x = plot), data = living_children, stat = "identity") +
guides(fill = FALSE) +
  scale_fill_manual(values = INTERACTPalette3)+
  ylab("Percent of total") +
  xlab("Response")

living_children.tb <- data.frame(Response = c("1", "2", "3", "4", "5", "6"),
           Frequence = as.numeric(table(d$living_children)), Percentage = round(as.numeric(prop.table(table(d$living_children)))*100,2)) 


kable(living_children.tb) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response Frequence Percentage
1 39 25.83
2 84 55.63
3 23 15.23
4 2 1.32
5 2 1.32
6 1 0.66

Q70: What is your current living arrangement? Do you live

Alone

#d$living_arrange_1[d$living_arrange_1==-7] <- NA

living_arrange_1 <- round(prop.table(table(factor(d$living_arrange_1)))*100,2)
living_arrange_1 <- as.data.frame(living_arrange_1)
living_arrange_1$group <- substring(row.names(living_arrange_1), 1)
living_arrange_1$group <- revalue(as.character(living_arrange_1$group), c("1" = "With other people",  "2" = "Alone"))

living_arrange_1$plot <- factor(living_arrange_1$group, living_arrange_1$group)

living_arrange_1.plot <- ggplot(living_arrange_1, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

living_arrange_1.plot + geom_histogram(aes(x = group), data = living_arrange_1, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

## Table- 
kable(data.frame(Response = c("With other people", "Alone"),
           Frequence = as.numeric(table(d$living_arrange_1)), Percentage = round(as.numeric(prop.table(table(d$living_arrange_1)))*100,2))) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response Frequence Percentage
With other people 237 84.34
Alone 44 15.66

With other people

Participants could choose multiple answers

#living_arrange  

# Create a vector with variable names
response = paste0("living_arrange_", 2:7)

# Empty vector to stor output
living_arrange_prop <- c()

# Calculate univariate proportions
for(i in response){
  living_arrange_prop[i] <- sum(d[,i]) / nrow(d)
}

# Transform
living_arrange_prop <- as.data.frame(living_arrange_prop)
living_arrange_prop$Response <- c("With a spouse (or partner)","With children","With grandchildren","With relatives or siblings?", "With friends", "With other people")

living_arrange_prop$plot<- factor(living_arrange_prop$Response, living_arrange_prop$Response)

ggplot(living_arrange_prop, aes(x = plot, y = living_arrange_prop)) + geom_bar(stat = "identity", fill = "#76D24A") + xlab("") + ylab("Percentage of participants who selected this answer") + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10))

living_arrange_prop$living_arrange_prop <- round(living_arrange_prop$living_arrange_prop*100,2)
living_arrange_prop$count <- c(sum(d$living_arrange_2), sum(d$living_arrange_3), sum(d$living_arrange_4),sum(d$living_arrange_5), sum(d$living_arrange_6), sum(d$living_arrange_7))

living_arrange_prop <- setcolorder(living_arrange_prop, c("Response", "count", "living_arrange_prop"))

colnames(living_arrange_prop) <- c("Response", "Count", "Percentage of participants who selected this answer")

living_arrange_prop <- living_arrange_prop[-c(4)]
kable(living_arrange_prop) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response Count Percentage of participants who selected this answer
living_arrange_2 With a spouse (or partner) 207 73.67
living_arrange_3 With children 88 31.32
living_arrange_4 With grandchildren 2 0.71
living_arrange_5 With relatives or siblings? 13 4.63
living_arrange_6 With friends 13 4.63
living_arrange_7 With other people 10 3.56

Q71: How many children under the age of 16 live in your household?

#children_household

ggplot(d, aes(x = d$children_household)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of children under 16 in household")

summary(d$children_household)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.4626  1.0000  3.0000

Q72: How many adults aged 16 or older live in your household including yourself?

ggplot(d, aes(x = d$adults_household)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of adults in household")

summary(d$children_household)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.4626  1.0000  3.0000

Q75: When did you move to your current residence?

#residence

residence <- as.integer(format(as.Date(d$residence),"%Y"))
time <- 2019 - residence

ggplot(d, aes(x = time)) + geom_histogram(na.rm=TRUE, binwidth = 1, fill="#76D24A") + xlab("Years since moving to current residence") 

summary(time)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.000   3.000   6.000   8.562  11.000  39.000

Q76: Were you born in Canada?

#born_can
born_can <- prop.table(table(factor(d$born_can, levels = c("1", "2"))))*100
born_can <- as.data.frame(born_can)
born_can$group <- substring(row.names(born_can), 1)
born_can$group <- revalue(as.character(born_can$group), c("1" = "Yes",  "2" = "No"))

born_can$plot <- factor(born_can$group, born_can$group)

born_can.plot <- ggplot(born_can, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

born_can.plot + geom_histogram(aes(x = plot), data = born_can, stat = "identity") +
      guides(fill = FALSE) +
      scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent of total") +
      xlab("Response")

## Table- 

born_can.tb <- data.frame(Response = c("Yes", "No"),
                          Frequence = as.numeric(table(d$born_can)), 
                          Percentage = round(as.numeric(prop.table(table(d$born_can)))*100,2))


kable(born_can.tb) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response Frequence Percentage
Yes 209 74.38
No 72 25.62

Q77: When did you move to Canada?

#move_can 

d$move_can[d$move_can==-7] <- NA
ggplot(d, aes(x = d$move_can)) + geom_histogram (na.rm=TRUE, binwidth = 1, fill="#76D24A") + xlab("Year of move to Canada")

summary(d$move_can)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1946    1973    1988    1986    2002    2017     209

Q78: Which category best describes your annual household income, taking into account all sources of income?

income <- round(prop.table(table(factor(d$income, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "77"))))*100,2)
income <- as.data.frame(income)
income$group <- substring(row.names(income), 1)
income$group <- revalue(as.character(income$group), c("1" = "No income", "2" = "$1 to $9,999", "3" = "$10,000 to $14,999", "4" = "$15,000 to $19,999", "5" = "$20,000 to $29,999", "6" = "$30,000 to $39,999", "7" = "$40,000 to $49,999", "8" = "$50,000 to $99,999", "9" = "$100,000 to $149,999", "10" = " $150,000 to $199,999", "11" = "$200,000 or more", "12" = "Don't know/prefer no answer"))

income$plot <- factor(income$group, income$group)

income.plot <- ggplot(income, aes(x = group, y = Freq, fill = plot)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values = rev(INTERACTfade)) +
      ylab("Percent of total") +
      xlab("")

income.plot + geom_histogram(aes(x = plot), data = income, stat = "identity")

income.tb <- as.factor(d$income)
income.tb <- summary(income.tb)
income.tb <- as.data.frame(income.tb)
income.tb$Var1 <- substring(row.names(income.tb), 1)

nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$income.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("1")
nval.df <- nval.df[-c(1)]

income.tb <- rbind(income.tb, nval.df)


income.tb$group <- revalue(as.character(income.tb$Var1), c("1" = "No income", "2" = "$1 to $9,999", "3" = "$10,000 to $14,999", "4" = "$15,000 to $19,999", "5" = "$20,000 to $29,999", "6" = "$30,000 to $39,999", "7" = "$40,000 to $49,999", "8" = "$50,000 to $99,999", "9" = "$100,000 to $149,999", "10" = " $150,000 to $199,999", "11" = "$200,000 or more", "77" = "Don't know/prefer no answer"))

## merge with existing prop table data used for plot above 
plot.income.tb <- merge(income, income.tb, by = "group")
plot.income.tb <- plot.income.tb[-c(4, 6)]
plot.income.tb <- setcolorder(plot.income.tb, c("group", "income.tb", "Freq", "Var1.x"))

plot.income.tb <- plot.income.tb %>% arrange(Var1.x)
plot.income.tb <- plot.income.tb[-c(4)]
colnames(plot.income.tb) <- c("Response", "N", "Percentage")


kable(plot.income.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
No income 0 0.00
$1 to $9,999 3 1.07
$10,000 to $14,999 2 0.71
$15,000 to $19,999 5 1.78
$20,000 to $29,999 9 3.20
$30,000 to $39,999 11 3.91
$40,000 to $49,999 16 5.69
$50,000 to $99,999 107 38.08
$100,000 to $149,999 65 23.13
$150,000 to $199,999 34 12.10
$200,000 or more 9 3.20
Don’t know/prefer no answer 20 7.12

Q79: To what extent does this annual household income allow you to satisfy your household’s needs?

#income_needs
income_needs <- round(prop.table(table(factor(d$income_needs, levels = c("1", "2", "3", "4", "77"))))*100,2)
income_needs <- as.data.frame(income_needs)
income_needs$group <- substring(row.names(income_needs), 1)
income_needs$group <- revalue(as.character(income_needs$group), c("1" = "Very well", "2" = "Well", "3" = "Not so well", "4" = "Not at all", "5" = "Don't know/prefer no answer"))

income_needs$group <- factor(income_needs$group, income_needs$group)

income_needs.plot <- ggplot(income_needs, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTshortfade) +
      ylab("Percent of total") +
      xlab("")
      
income_needs.plot + geom_histogram(aes(x = group), data = income_needs, stat = "identity") 

## Table- 
income_needs.tb <- data.frame(Response = c("Very well", "Well", "Not so well", "Not at all", "Don't know/prefer no answer"),
           Frequence = as.numeric(table(d$income_needs)), Percentage = round(as.numeric(prop.table(table(d$income_needs)))*100,2))

kable(income_needs.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response Frequence Percentage
Very well 108 38.43
Well 124 44.13
Not so well 33 11.74
Not at all 5 1.78
Don’t know/prefer no answer 11 3.91

Q80: What is your highest education level?

#education
education <- prop.table(table(factor(d$education, levels = c("1", "2", "3", "4","5", "77"))))*100
education <- as.data.frame(education)
education$group <- substring(row.names(education), 1)
education$group <- revalue(as.character(education$group), c("1" = "Primary/Elementary school", "2" = "Secondary school", "3" = "Trade/Technical school or college diploma", "4" = "University degree", "5" = "Graduate degree", "6" ="I don't know/Prefer not to answer"))

education$group <- factor(education$group, education$group)

education.plot <- ggplot(education, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=rev(INTERACTshortfade))  +
      ylab("Percent of total") +
      xlab("")
      
education.plot + geom_histogram(aes(x = group), data = education, stat = "identity") 

#table 
education.tb <- as.factor(d$education)
education.tb <- summary(education.tb)
education.tb <- as.data.frame(education.tb)
education.tb$Var1 <- substring(row.names(education.tb), 1)

nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$education.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("1", "6")
nval.df <- nval.df[-c(1)]

education.tb <- rbind(education.tb, nval.df)

education.tb$group <- revalue(as.character(education.tb$Var1), c("1" = "Primary/Elementary school", "2" = "Secondary school", "3" = "Trade/Technical school or college diploma", "4" = "University degree", "5" = "Graduate degree", "77" ="I don't know/Prefer not to answer"))

## merge with existing prop table data used for plot above 
plot.education.tb <- merge(education, education.tb, by = "group")
plot.education.tb <- plot.education.tb %>% arrange(Var1.x)
plot.education.tb <- plot.education.tb[-c(2,5)]
plot.education.tb <- setcolorder(plot.education.tb, c("group", "education.tb", "Freq"))
colnames(plot.education.tb) <- c("Response", "N", "Percentage")

kable(plot.education.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Primary/Elementary school 0 NaN

Q81: What is your current employment status?

#employment
employment <- prop.table(table(factor(d$employment, levels = c("1", "2", "3", "4","5", "6"))))*100
employment <- as.data.frame(employment)
employment$group <- substring(row.names(employment), 1)
employment$group <- revalue(as.character(employment$group), c("1" = "Retired and not working", "2" = "Employed full-time", "3" = "Employed part-time", "4" = "Unemployed and looking for work", "5" = "Unemployed and not looking for work", "6" ="Other"))

employment$group <- factor(employment$group, employment$group)

employment.plot <- ggplot(employment, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x  = element_text(size= 12, angle=0, vjust=.6))  + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) +
      guides(fill = FALSE) +
      scale_fill_manual(values=INTERACTPaletteSet)  +
      ylab("Percent of total") +
      xlab("")
      
employment.plot + geom_histogram(aes(x = group), data = employment, stat = "identity") 

#table 
employment.tb <- as.factor(d$employment)
employment.tb <- summary(employment.tb)
employment.tb <- as.data.frame(employment.tb)
employment.tb$Var1 <- substring(row.names(employment.tb), 1)

nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$employment.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("5")
nval.df <- nval.df[-c(1)]

employment.tb <- rbind(employment.tb, nval.df)

employment.tb$group <- revalue(as.character(employment.tb$Var1), c("1" = "Retired and not working", "2" = "Employed full-time", "3" = "Employed part-time", "4" = "Unemployed and looking for work", "5" = "Unemployed and not looking for work", "6" ="Other"))

## merge with existing prop table data used for plot above 
plot.employment.tb <- merge(employment, employment.tb, by = "group")
plot.employment.tb <- plot.employment.tb %>% arrange(Var1.x)
plot.employment.tb <- plot.employment.tb[-c(2,5)]
plot.employment.tb <- setcolorder(plot.employment.tb, c("group", "employment.tb", "Freq"))
colnames(plot.employment.tb) <- c("Response", "N", "Percentage")

kable(plot.employment.tb) %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Response N Percentage
Unemployed and not looking for work 0 NaN

Age distribution


# Create empty variable to store categorized age
d$age_cat <- NA

# Categorize age variable
d[d$age %in% c(18:24),"age_cat"] <- "18-24"
d[d$age %in% c(25:34),"age_cat"] <- "25-34"
d[d$age %in% c(35:44),"age_cat"] <- "35-44"
d[d$age %in% c(45:54),"age_cat"] <- "45-54"
d[d$age %in% c(55:64),"age_cat"] <- "55-64"
d[d$age %in% c(65:74),"age_cat"] <- "65-74"
d[d$age %in% c(75:100),"age_cat"] <-"75+"


# Plot age category bar chart
ggplot(data = d, aes(x = age_cat)) +
  geom_bar(fill = "#76D24A", aes(y = (..count..)/sum(..count..))) +
  labs(x = "Age", y = "Percent of total") +
  theme_bw()

#Create count table 
age <- data.frame(Ages = names(table(d$age_cat)),
                    count = as.vector(table(d$age_cat)),
                    Percentage =as.vector(round(prop.table(table(d$age_cat))*100,2)))
# Print table
kable(age)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

########################### returning 

# # Create empty variable to store categorized age
# o$age_cat <- NA
# 
# #Categorize age variable
# o[o$age %in% c(18:24),"age_cat"] <- "18-24"
# o[o$age %in% c(25:34),"age_cat"] <- "25-34"
# o[o$age %in% c(35:44),"age_cat"] <- "35-44"
# o[o$age %in% c(45:54),"age_cat"] <- "45-54"
# o[o$age %in% c(55:64),"age_cat"] <- "55-64"
# o[o$age %in% c(65:74),"age_cat"] <- "65-74"
# o[o$age %in% c(75:100),"age_cat"] <-"75+"
# 
# 
# # Plot age category bar chart
# ggplot(data = o, aes(x = age_cat)) +
#   geom_bar(fill = "#76D24A", aes(y = (..count..)/sum(..count..))) +
#   labs(x = "Age", y = "Percent of total") +
#   theme_bw()
# 
# #Create count table 
# age <- data.frame(Ages = names(table(o$age_cat)),
#                     count = as.vector(table(o$age_cat)),
#                     Percentage =as.vector(round(prop.table(table(o$age_cat))*100,2)))
# # Print table
# kable(age)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")